Search results for: closed frequent item set mining.

Authors: Seyed Hossein Iranmanesh, Zahra Mokhtari

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Estimation time and cost of work completion in a project and follow up them during execution are contributors to success or fail of a project, and is very important for project management team. Delivering on time and within budgeted cost needs to well managing and controlling the projects. To dealing with complex task of controlling and modifying the baseline project schedule during execution, earned value management systems have been set up and widely used to measure and communicate the real physical progress of a project. But it often fails to predict the total duration of the project. In this paper data mining techniques is used predicting the total project duration in term of Time Estimate At Completion-EAC (t). For this purpose, we have used a project with 90 activities, it has updated day by day. Then, it is used regular indexes in literature and applied Earned Duration Method to calculate time estimate at completion and set these as input data for prediction and specifying the major parameters among them using Clem software. By using data mining, the effective parameters on EAC and the relationship between them could be extracted and it is very useful to manage a project with minimum delay risks. As we state, this could be a simple, safe and applicable method in prediction the completion time of a project during execution.

Keywords: Data Mining Techniques, Earned Duration Method, Earned Value, Estimate At Completion.

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Authors: Anamika Gupta, Naveen Kumar, Vasudha Bhatnagar

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This paper focuses on analyzing medical diagnostic data using classification rules in data mining and context reduction in formal concept analysis. It helps in finding redundancies among the various medical examination tests used in diagnosis of a disease. Classification rules have been derived from positive and negative association rules using the Concept lattice structure of the Formal Concept Analysis. Context reduction technique given in Formal Concept Analysis along with classification rules has been used to find redundancies among the various medical examination tests. Also it finds out whether expensive medical tests can be replaced by some cheaper tests.

Keywords: Data Mining, Formal Concept Analysis, Medical Data, Negative Classification Rules.

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Authors: Fahad Shahbaz Khan, Rao Muhammad Anwer, Olof Torgersson

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The vast amount of information hidden in huge databases has created tremendous interests in the field of data mining. This paper examines the possibility of using data clustering techniques in oral medicine to identify functional relationships between different attributes and classification of similar patient examinations. Commonly used data clustering algorithms have been reviewed and as a result several interesting results have been gathered.

Keywords: Oral Medicine, Cluto, Data Clustering, Data Mining.

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Authors: Zunaira Asif, Zhi Chen

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The sustainable measures on air quality management are recognized as one of the most serious environmental concerns in the mining region. The mining operations emit various types of pollutants which have significant impacts on the environment. This study presents a stochastic control strategy by developing the air pollution control model to achieve a cost-effective solution. The optimization method is formulated to predict the cost of treatment using linear programming with an objective function and multi-constraints. The constraints mainly focus on two factors which are: production of metal should not exceed the available resources, and air quality should meet the standard criteria of the pollutant. The applicability of this model is explored through a case study of an open pit metal mine, Utah, USA. This method simultaneously uses meteorological data as a dispersion transfer function to support the practical local conditions. The probabilistic analysis and the uncertainties in the meteorological conditions are accomplished by Monte Carlo simulation. Reasonable results have been obtained to select the optimized treatment technology for PM_2.5, PM₁₀, NO_x, and SO₂. Additional comparison analysis shows that baghouse is the least cost option as compared to electrostatic precipitator and wet scrubbers for particulate matter, whereas non-selective catalytical reduction and dry-flue gas desulfurization are suitable for NO_x and SO₂ reduction respectively. Thus, this model can aid planners to reduce these pollutants at a marginal cost by suggesting control pollution devices, while accounting for dynamic meteorological conditions and mining activities.

Keywords: Air pollution, linear programming, mining, optimization, treatment technologies.

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Authors: Nevine M. Labib, Michael N. Malek

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Data Mining aims at discovering knowledge out of data and presenting it in a form that is easily comprehensible to humans. One of the useful applications in Egypt is the Cancer management, especially the management of Acute Lymphoblastic Leukemia or ALL, which is the most common type of cancer in children. This paper discusses the process of designing a prototype that can help in the management of childhood ALL, which has a great significance in the health care field. Besides, it has a social impact on decreasing the rate of infection in children in Egypt. It also provides valubale information about the distribution and segmentation of ALL in Egypt, which may be linked to the possible risk factors. Undirected Knowledge Discovery is used since, in the case of this research project, there is no target field as the data provided is mainly subjective. This is done in order to quantify the subjective variables. Therefore, the computer will be asked to identify significant patterns in the provided medical data about ALL. This may be achieved through collecting the data necessary for the system, determimng the data mining technique to be used for the system, and choosing the most suitable implementation tool for the domain. The research makes use of a data mining tool, Clementine, so as to apply Decision Trees technique. We feed it with data extracted from real-life cases taken from specialized Cancer Institutes. Relevant medical cases details such as patient medical history and diagnosis are analyzed, classified, and clustered in order to improve the disease management.

Keywords: Data Mining, Decision Trees, Knowledge Discovery, Leukemia.

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: Data mining, hybrid storage system, recurrent neural network, support vector machine.

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Authors: Isti Surjandari, Riara Novita

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Maintenance is one of the most important activities in the shipyard industry. However, sometimes it is not supported by adequate services from the shipyard, where inaccuracy in estimating the duration of the ship maintenance is still common. This makes estimation of ship maintenance duration is crucial. This study uses Data Mining approach, i.e., CART (Classification and Regression Tree) to estimate the duration of ship maintenance that is limited to dock works or which is known as dry docking. By using the volume of dock works as an input to estimate the maintenance duration, 4 classes of dry docking duration were obtained with different linear model and job criteria for each class. These linear models can then be used to estimate the duration of dry docking based on job criteria.

Keywords: Classification and regression tree (CART), data mining, dry docking, maintenance duration.

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Authors: Moth'd Belal. Al-Daoud

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Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the k-means algorithm. Solutions obtained from this technique are dependent on the initialization of cluster centers. In this article we propose a new algorithm to initialize the clusters. The proposed algorithm is based on finding a set of medians extracted from a dimension with maximum variance. The algorithm has been applied to different data sets and good results are obtained.

Keywords: clustering, k-means, data mining.

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Authors: B. A. Parate, C. P. Shetty

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This research article describes the evaluation methodology of an internal ballistics of multi-perforated grain in a closed vessel (CV). The propellant testing in a CV is conducted to characterize the propellants and to ascertain the various internal ballistic parameters. The assessment of an internal ballistics plays a very crucial role for suitability of its use in the selection for a given particular application. The propellant used in defense sectors has to satisfy the user requirements as per laid down specifications. The outputs from CV evaluation of multi-propellant grain are maximum pressure of 226.75 MPa, differentiation of pressure with respect to time of 36.99 MPa/ms, average vivacity of 9.990×10-4/MPa ms, force constant of 933.9 J/g, rise time of 9.85 ms, pressure index of 0.878 including burning coefficient of 0.2919. This paper addresses an internal ballistic of multi-perforated grain, propellant selection, its calculation, and evaluation of various parameters in a CV testing. For the current analysis, the propellant is evaluated in 100 cc CV with propellant mass 20 g. The loading density of propellant is 0.2 g/cc. The method for determination of internal ballistic properties consists of burning of propellant mass under constant volume.

Keywords: Burning rate, closed vessel, force constant, internal ballistic, loading density, maximum pressure, multi-propellant grain, propellant, rise time, vivacity.

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Authors: I. Cano-Aguilera, B. E. Rubio-Campos, G. De la Rosa, A. F. Aguilera-Alvarado

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Mining tailings represent a generating source of rich heavy metal material with a potential danger the public health and the environment, since these metals, under certain conditions, can leach and contaminate aqueous systems that serve like supplying potable water sources. The strategy for this work is based on the observation, experimentation and the simulation that can be obtained by binding real answers of the hydrodynamic behavior of metals leached from mining tailings, and the applied mathematics that provides the logical structure to decipher the individual effects of the general physicochemical phenomenon. The case of study presented herein focuses on mining tailings deposits located in Monte San Nicolas, Guanajuato, Mexico, an abandoned mine. This was considered the contamination source that under certain physicochemical conditions can favor the metal leaching, and its transport towards aqueous systems. In addition, the cartography, meteorology, geology and the hydrodynamics and hydrological characteristics of the place, will be helpful in determining the way and the time in which these systems can interact. Preliminary results demonstrated that arsenic presents a great mobility, since this one was identified in several superficial aqueous systems of the micro watershed, as well as in sediments in concentrations that exceed the established maximum limits in the official norms. Also variations in pH and potential oxide-reduction were registered, conditions that favor the presence of different species from this element its solubility and therefore its mobility.

Keywords: Arsenic, mining tailings, transport.

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Authors: Akinori Ozaki

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In this study, in order to clarify wind-induced phenomena, especially vertical mixing of density stratification in a closed water area with floating-leaved plants, we conducted hydraulic experiments on wind flow characteristics, wind wave characteristics, entrainment phenomena and turbulent structure by using a wind tunnel test tank and simulated floating-leaved plants. From the experimental results of wind flow and wind wave characteristics, we quantified the impact of the occupancy rate of the plants on their resistance characteristics. From the experimental results of entrainment phenomena, we defined the parameter that could explain the magnitude of mixing between the density stratifications, and quantified the impact of the occupancy rate on vertical mixing between stratifications. From the experimental results of the turbulent structure of the upper layer, we clarified the differences in small-scale turbulence components at each occupancy rate and quantified the impact of the occupancy rate on the turbulence characteristics. For a summary of this study, we theoretically quantified wind-induced entrainment phenomena in a closed water area with luxuriant growth of floating-leaved plants. The results indicated that the impact of luxuriant growth of floating-leaved plants in a closed water body could be seen in the difference in small-scale fluid characteristics, and these characteristics could be expressed using the small-scale turbulent components.

Keywords: Density Stratification, Floating-leaved Plant, Wind-induced Entrainment Phenomenon, Turbulent Structure

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Authors: L. R. Ferreira, R. C. G. Bianchi, L. C.R. Ferreira, C. M. Galhardi, E. P. Baciuk, L. H. Oliveira

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Inhalation hazards are associated with potentially injurious exposure and increased risk for lung diseases, within the bauxite mining industry, especially for the smelter workers. Smoking is related to decreased lung function and leads to chronic lung diseases. This study had the objective to evaluate whether smoking is related to functional and radiographic respiratory changes in retired bauxite mining workers. Methods: This was a retrospective and cross-sectional study involving the analysis of database information of 140 retired bauxite mining workers from Poços de Caldas-MG evaluated at Worker’s Health Reference Center and at the Social Security Brazilian National Institute, from July 1^st, 2015 until June 30^th, 2016. The workers were divided into three groups: non-smokers (n = 47), ex-smokers (n = 46), and smokers (n = 47). The data included: age, gender, spirometry results, and the presence or not of pulmonary pleural and/or parenchymal changes in chest radiographs. Chi-Squared test was used (p < 0,05). Results: In the smokers’ group, 83% of spirometry tests and 64% of chest x-rays were altered. In the non-smokers’ group, 19% of spirometry tests and 13% of chest x-rays were altered. In the ex-smokers’ group, 35% of spirometry tests and 30% of chest x-rays were altered. Most of the results were statistically significant. Results demonstrated a significant difference between smokers’ and non-smokers’ groups in regard to spirometric and radiographic pulmonary alterations. Ex-smokers’ and non-smokers’ group demonstrated better results when compared to the smokers’ group in relation to altered spirometry and radiograph findings. These data may contribute to planning strategies to enhance smoking cessation programs within the bauxite mining industry.

Keywords: Bauxite mining, spirometry, chest radiography, smoking.

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Authors: Gulshan Sharma

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This article presents the design of optimal automatic generation control (AGC) based on full state feedback control for a multi-area interconnected power system. An extra high voltage AC transmission line in parallel with a high voltage DC link is considered as an area interconnection between the areas. The optimal AGC are designed and implemented in the wake of 1% load perturbation in one of the areas and the system dynamic response plots for various system states are obtained to investigate the system dynamic performance. The pattern of closed-loop eigenvalues are also determined to analyze the system stability. From the investigations carried out in the work, it is revealed that the dynamic performance of the system under consideration has an appreciable improvement when a high voltage DC line is paralleled with an extra high voltage AC line as an interconnection between the areas. The investigation of closed-loop eigenvalues reveals that the system stability is ensured in all case studies carried out with the designed optimal AGC.

Keywords: Automatic generation control, area control error, DC link, optimal AGC regulator, closed-loop eigenvalues.

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Authors: Saleh Mesbah Elkaffas, Ahmed A. Toony

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This paper details the application of a genetic programming framework for induction of useful classification rules from a database of income statements, balance sheets, and cash flow statements for North American public companies. Potentially interesting classification rules are discovered. Anomalies in the discovery process merit further investigation of the application of genetic programming to the dataset for the problem domain.

Keywords: Genetic programming, data mining classification rule.

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Authors: JanuszKwaśniewski, IreneuszDominik, KrzysztofLalik

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The knowledge about rock layers thickness,especially above drilled mining pavements is crucial for workers safety. The measuring systems used nowadays are generally imperfect and there is a strong demand for improvement. The application of a new type of a measurement system called Self-excited Acoustical System is presentedin the paper. The system was applied until now to monitor stress changes in metal and concrete constructions. The change in measurement methodology resulted in possibility of measuring the thickness of the rocks above the tunnels as well as thickness of a singular rocklayer. The idea is to find two resonance frequencies of the self-exited system,which consists of a vibration exciter and vibration receiver placed at a distance, which are coupled with a proper power amplifier, and which operate in a closed loop with a positive feedback. The resonance with the higher amplitude determines thickness of the whole rock, whereas the lower amplitude resonance indicates thickness of a singular layer. The results of the laboratory tests conducted on a group of different rock materials are also presented.

Keywords: Autooscillator, non-destructive testing, rock thickness measurement.

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Authors: Mathias Marquardt, Peter Dünow, Sandra Baßler

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The paper describes the use of subspace based identification methods for auto tuning of a state space control system. The plant is an unstable but self balancing transport robot. Because of the unstable character of the process it has to be identified from closed loop input-output data. Based on the identified model a state space controller combined with an observer is calculated. The subspace identification algorithm and the controller design procedure is combined to a auto tuning method. The capability of the approach was verified in a simulation experiments under different process conditions.

Keywords: Auto tuning, balanced robot, closed loop identification, subspace identification.

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Authors: Vipul M. Patel, H. B. Mehta

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Closed Loop Pulsating Heat Pipe (CLPHP) is a passive two-phase heat transfer device having potential to achieve high heat transfer rates over conventional cooling techniques. It is found in electronics cooling due to its outstanding characteristics such as excellent heat transfer performance, simple, reliable, cost effective, compact structure and no external mechanical power requirement etc. Comprehensive understanding of the thermo-hydrodynamic mechanism of CLPHP is still lacking due to its contradictory results available in the literature. The present paper discusses the experimental study on 9 turn CLPHP. Inner and outer diameters of the copper tube are 2 mm and 4 mm respectively. The lengths of the evaporator, adiabatic and condenser sections are 40 mm, 100 mm and 50 mm respectively. Water is used as working fluid. The Filling Ratio (FR) is kept as 50% throughout the investigations. The gravitational effect is studied by placing the evaporator heater at different orientations such as horizontal (90 degree), vertical top (180 degree) and bottom (0 degree) as well as inclined top (135 degree) and bottom (45 degree). Heat input is supplied in the range of 10-50 Watt. Heat transfer mechanism is natural convection in the condenser section. Vacuum pump is used to evacuate the system up to 10^-5 bar. The results demonstrate the influence of input heat flux and gravity on the thermal performance of the CLPHP.

Keywords: Closed loop pulsating heat pipe, gravity, heat input, orientation.

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Authors: Seyed Mohammad Nasir Mousavi, Pasha Hejazi, Maryam Ebrahimian Dehkordi

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In order to study the Mutual effect of genotype × environment for the percent of oil index in sunflower items, an experiment was accomplished form complete random block designs in four iteration and was four diverse researching station comprising Esfahan, Birjand, Sari, and Karaj. Complex variance analysis showed that there is an important diversity between the items under investigation. The results relevant the coefficient variation of items Azargol and Vidoc has respectively allocated the minimum coefficient of variations. According to the results extrapolated from Shokla stability variance, the Items Brocar, Allison and Fabiola, are among the stable genotypes for oil percent respectively. In the biplot GGE, the location under investigations divided in two superenvironments, first one comprised of locations naming Esfahan, Karaj, and Birjand, and second one were such a location as Sari. By this point of view, in the first super-environment, the Item Fabiola and in the second Almanzor item was among the best items and crops.

Keywords: Sunflower, Stability, GGE biplot, Super- Environment.

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Authors: Mustafa Qaseem Hielat, Ahmad Mohammad Al-Shabatat

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Jordan exerts many efforts to nurture their academically gifted students in special schools since 2001. During the past nine years of launching these schools, their learning and excellence environments were believed to be distinguished compared to public schools. This study investigated the environments of gifted students compared with other non-gifted, using a survey instrument that measures the dimensions of family, peers, teachers, school- support, society, and resources –dimensions rooted deeply in supporting gifted education, learning, and achievement. A total number of 109 were selected from excellence schools for academically gifted students, and 119 non-gifted students were selected from public schools. Around 8.3% of the non-gifted students reported that they “Never" received any support from their surrounding environments, 14.9% reported “Seldom" support, 23.7% reported “ Often" support, 26.0% reported “Frequent" support, and 32.8% reported “Very frequent" support. Where the gifted students reported more “Never" support than the non-gifted did with 11.3%, “Seldom" support with 15.4%, “Often" support with 26.6%, “Frequent" support with 29.0%, and reported “Very frequent" support less than the non-gifted students with 23.6%. Unexpectedly, statistical differences were found between the two groups favoring non-gifted students in perception of their surrounding environments in specific dimensions, namely, school- support, teachers, and society. No statistical differences were found in the other dimensions of the survey, namely, family, peers, and resources. As the differences were found in teachers, school- support, and society, the nurturing environments for the excellence schools need to be revised to adopt more creative teaching styles, rich school atmosphere and infrastructures, interactive guiding for the students and their parents, promoting for the excellence environments, and re-build successful identification models. Thus, families, schools, and society should increase their cooperation, communication, and awareness of the gifted supportive environments. However, more studies to investigate other aspects of promoting academic giftedness and excellence are recommended.

Keywords: Academic giftedness, Supportive environment, Excellence schools, Gifted grouping, Gifted nurturing.

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Authors: Vasudha Bhatnagar, Ahmed Sultan Al-Hegami, Naveen Kumar

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Rule Discovery is an important technique for mining knowledge from large databases. Use of objective measures for discovering interesting rules leads to another data mining problem, although of reduced complexity. Data mining researchers have studied subjective measures of interestingness to reduce the volume of discovered rules to ultimately improve the overall efficiency of KDD process. In this paper we study novelty of the discovered rules as a subjective measure of interestingness. We propose a hybrid approach based on both objective and subjective measures to quantify novelty of the discovered rules in terms of their deviations from the known rules (knowledge). We analyze the types of deviation that can arise between two rules and categorize the discovered rules according to the user specified threshold. We implement the proposed framework and experiment with some public datasets. The experimental results are promising.

Keywords: Knowledge Discovery in Databases (KDD), Interestingness, Subjective Measures, Novelty Index.

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: Data mining, knowledge discovery, machine learning, similarity measurement, supervised classification.

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Authors: Pranee Pathomchaiwat

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This research aims to study tourism data and behavior of foreign tourists visited Wat Phrachetuponwimolmangkalaram (Wat Po) Sample groups are tourists who visited inside the temple, during February, March, April and May 2013. Tools used in the research are questionnaires constructed by the researcher, and samples are dawn by Convenience sampling. There are 207 foreign tourists who are willing to be respondents. Statistics used are percentage, average mean and standard deviation. The results of the research reveal that: A. General Data of Respondents The foreign tourists who visited the temple are mostly female (57.5 %), most respondents are aged between 20-29 years (37.2%). Most respondents live in Europe (62.3%), most of them got the Bachelor’s degree (40.1%), British are mostly found (16.4%), respondents who are students are also found (23.2%), and Christian are mostly found (60.9%). B. Tourists’ Behavior While Visiting the Temple Compound. The result shows that the respondents came with family (46.4%), have never visited the temples (40.6%), and visited once (42 %). It is found that the foreign tourists’ inappropriate behavior are wearing revealing attires (58.9%), touching or getting closed to the monks (55.1%), and speaking loudly (46.9%) respectively. The respondents’ outstanding objectives are to visit inside the temple (57.5%), to pay respect to the Reclining Buddha Image in the Viharn (44.4%) and to worship the Buddha image in the Phra Ubosod (37.7%) respectively. C. The Respondents’ Self-evaluation of Performance It is found that over all tourists evaluated themselves in the highest level averaged 4.40. When focusing on each item, it is shown that they evaluated themselves in the highest level on obeying the temple staff averaged 4.57, and cleanness concern of the temple averaged 4.52, well-behaved performance during the temple visit averaged 4.47 respectively.

Keywords: Deportment, Traveler

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Authors: A.R. Aswatha, T. Basavaraju, S. Sandeep Kumar

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Timing driven physical design, synthesis, and optimization tools need efficient closed-form delay models for estimating the delay associated with each net in an integrated circuit (IC) design. The total number of nets in a modern IC design has increased dramatically and exceeded millions. Therefore efficient modeling of interconnection is needed for high speed IC-s. This paper presents closed–form expressions for RC and RLC interconnection trees in current mode signaling, which can be implemented in VLSI design tool. These analytical model expressions can be used for accurate calculation of delay after the design clock tree has been laid out and the design is fully routed. Evaluation of these analytical models is several orders of magnitude faster than simulation using SPICE.

Keywords: IC design, RC/RLC Interconnection, VLSI Systems.

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Authors: Niti Kammuang-lue, Kritsada On-ai, Phrut Sakulchangsatjatai, Pradit Terdtoon

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The objectives of this paper are to investigate effects of dimensionless numbers on thermal performance of the vertical closed-loop pulsating heat pipe (VCLPHP) and to establish a correlation to predict the thermal performance of the VCLPHP. The CLPHPs were made of long copper capillary tubes with inner diameters of 1.50, 1.78, and 2.16mm and bent into 26 turns. Then, both ends were connected together to form a loop. The evaporator, adiabatic, and condenser sections length were equal to 50 and 150 mm. R123, R141b, acetone, ethanol, and water were chosen as variable working fluids with constant filling ratio of 50% by total volume. Inlet temperature of heating medium and adiabatic section temperature was constantly controlled at 80 and 50^oC, respectively. Thermal performance was represented in a term of Kutateladze number (Ku). It can be concluded that when Prandtl number of liquid working fluid (Pr_l), and Karman number (Ka) increases, thermal performance increases. On contrary, when Bond number (Bo), Jacob number (Ja), and Aspect ratio (L_e/D_i) increases, thermal performance decreases. Moreover, the correlation to predict more precise thermal performance has been successfully established by analyzing on all dimensionless numbers that have effect on the thermal performance of the VCLPHP.

Keywords: Vertical closed-loop pulsating heat pipe, working fluid, thermal performance, dimensionless parameter.

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Authors: Rekha Kandwal, Kamal K.Bharadwaj

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Knowledge is indispensable but voluminous knowledge becomes a bottleneck for efficient processing. A great challenge for data mining activity is the generation of large number of potential rules as a result of mining process. In fact sometimes result size is comparable to the original data. Traditional data mining pruning activities such as support do not sufficiently reduce the huge rule space. Moreover, many practical applications are characterized by continual change of data and knowledge, thereby making knowledge voluminous with each change. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. Michalski & Winston proposed Censored Production Rules (CPRs), as an extension of production rules, that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to the rule. Such rules are employed in situations in which the conditional statement 'If P Then D' holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence, are tight or there is simply no information available as to whether it holds or not. Thus the 'If P Then D' part of the CPR expresses important information while the Unless C part acts only as a switch changes the polarity of D to ~D. In this paper a scheme based on Dempster-Shafer Theory (DST) interpretation of a CPR is suggested for discovering CPRs from the discovered flat PRs. The discovery of CPRs from flat rules would result in considerable reduction of the already discovered rules. The proposed scheme incrementally incorporates new knowledge and also reduces the size of knowledge base considerably with each episode. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested cumulative learning scheme would be useful in mining data streams.

Keywords: Censored production rules, cumulative learning, data mining, machine learning.

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Authors: P. Jomsri

Abstract:

Currently, web usage make a huge data from a lot of user attention. In general, proxy server is a system to support web usage from user and can manage system by using hit rates. This research tries to improve hit rates in proxy system by applying data mining technique. The data set are collected from proxy servers in the university and are investigated relationship based on several features. The model is used to predict the future access websites. Association rule technique is applied to get the relation among Date, Time, Main Group web, Sub Group web, and Domain name for created model. The results showed that this technique can predict web content for the next day, moreover the future accesses of websites increased from 38.15% to 85.57 %. This model can predict web page access which tends to increase the efficient of proxy servers as a result. In additional, the performance of internet access will be improved and help to reduce traffic in networks.

Keywords: Association rule, proxy server, data mining.

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Authors: M. Govindarajan, R. M. Chandrasekaran

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Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms.

Keywords: Back propagation, classification accuracy, textmining, time complexity.

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Authors: M. Govindarajan

Abstract:

Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In decision tree approach is most useful in classification problem. With this technique, tree is constructed to model the classification process. There are two basic steps in the technique: building the tree and applying the tree to the database. This paper describes a proposed C5.0 classifier that performs rulesets, cross validation and boosting for original C5.0 in order to reduce the optimization of error ratio. The feasibility and the benefits of the proposed approach are demonstrated by means of medial data set like hypothyroid. It is shown that, the performance of a classifier on the training cases from which it was constructed gives a poor estimate by sampling or using a separate test file, either way, the classifier is evaluated on cases that were not used to build and evaluate the classifier are both are large. If the cases in hypothyroid.data and hypothyroid.test were to be shuffled and divided into a new 2772 case training set and a 1000 case test set, C5.0 might construct a different classifier with a lower or higher error rate on the test cases. An important feature of see5 is its ability to classifiers called rulesets. The ruleset has an error rate 0.5 % on the test cases. The standard errors of the means provide an estimate of the variability of results. One way to get a more reliable estimate of predictive is by f-fold –cross- validation. The error rate of a classifier produced from all the cases is estimated as the ratio of the total number of errors on the hold-out cases to the total number of cases. The Boost option with x trials instructs See5 to construct up to x classifiers in this manner. Trials over numerous datasets, large and small, show that on average 10-classifier boosting reduces the error rate for test cases by about 25%.

Keywords: C5.0, Error Ratio, text mining, training data, test data.

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Authors: Murat Koklu, Yavuz Unal

Abstract:

Data mining can be called as a technique to extract information from data. It is the process of obtaining hidden information and then turning it into qualified knowledge by statistical and artificial intelligence technique. One of its application areas is medical area to form decision support systems for diagnosis just by inventing meaningful information from given medical data. In this study a decision support system for diagnosis of illness that make use of data mining and three different artificial intelligence classifier algorithms namely Multilayer Perceptron, Naive Bayes Classifier and J.48. Pima Indian dataset of UCI Machine Learning Repository was used. This dataset includes urinary and blood test results of 768 patients. These test results consist of 8 different feature vectors. Obtained classifying results were compared with the previous studies. The suggestions for future studies were presented.

Keywords: Artificial Intelligence, Classifiers, Data Mining, Diabetic Patients.

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Authors: Carlo Bellettini, Alessandro Marchetto, Andrea Trentini

Abstract:

Web applications have become very complex and crucial, especially when combined with areas such as CRM (Customer Relationship Management) and BPR (Business Process Reengineering), the scientific community has focused attention to Web applications design, development, analysis, and testing, by studying and proposing methodologies and tools. This paper proposes an approach to automatic multi-dimensional concern mining for Web Applications, based on concepts analysis, impact analysis, and token-based concern identification. This approach lets the user to analyse and traverse Web software relevant to a particular concern (concept, goal, purpose, etc.) via multi-dimensional separation of concerns, to document, understand and test Web applications. This technique was developed in the context of WAAT (Web Applications Analysis and Testing) project. A semi-automatic tool to support this technique is currently under development.

Keywords: Concepts Analysis, Concerns Mining, Multi-Dimensional Separation of Concerns, Impact Analysis.

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