Search results for: data protection laws

Authors: Dong Hoon Lim

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A series of microarray experiments produces observations of differential expression for thousands of genes across multiple conditions. Principal component analysis(PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. PCA, which can be implemented via a singular value decomposition(SVD), is useful for analysis of microarray data. For application of PCA using SVD we use the DNA microarray data for the small round blue cell tumors(SRBCT) of childhood by Khan et al.(2001). To decide the number of components which account for sufficient amount of information we draw scree plot. Biplot, a graphic display associated with PCA, reveals important features that exhibit relationship between variables and also the relationship of variables with observations.

Keywords: Principal component analysis, singular value decomposition, microarray data, SRBCT

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Authors: Ioan Rusu

Abstract:

In the operation of power transformers of 110 kV/MV from substations, these are traveled by fault current resulting from MV line damage. Defect electrical contacts are heated when they are travelled from fault currents. In the case of high temperatures when 135 °C is reached, the electrical insulating oil in the vicinity of the electrical faults comes into contact with these contacts releases gases, and activates the electrical protection. To avoid auto-flammability of electro-insulating oil, we designed a security system thermal of electrical contact defects by pouring fire-resistant polyurethane foam, mastic or mortar fire inside a cardboard electro-insulating cylinder. From practical experience, in the exploitation of power transformers of 110 kV/MT in oil electro-insulating were recorded some passing disconnecting commanded by the gas protection at internal defects. In normal operation and in the optimal load, nominal currents do not require thermal secure contacts inside electrical transformers, contacts are made at the fabrication according to the projects or to repair by solder. In the case of external short circuits close to the substation, the contacts inside electrical transformers, even if they are well made in sizes of Rcontact = 10^‑6 Ω, are subjected to short-circuit currents of the order of 10 kA-20 kA which lead to the dissipation of some significant second-order electric powers, 100 W-400 W, on contact. At some internal or external factors which action on electrical contacts, including electrodynamic efforts at short-circuits, these factors could be degraded over time to values in the range of 10^-4 Ω to 10^-5 Ω and if the action time of protection is great, on the order of seconds, power dissipation on electrical contacts achieve high values of 1,0 kW to 40,0 kW. This power leads to strong local heating, hundreds of degrees Celsius and can initiate self-ignition and burning oil in the vicinity of electro-insulating contacts with action the gas relay. Degradation of electrical contacts inside power transformers may not be limited for the duration of their operation. In order to avoid oil burn with gas release near electrical contacts, at short-circuit currents 10 kA-20 kA, we have outlined the following solutions: covering electrical contacts in fireproof materials that would avoid direct burn oil at short circuit and transmission of heat from electrical contact along the conductors with heat dissipation gradually over time, in a large volume of cooling. Flame retardant materials are: polyurethane foam, mastic, cement (concrete). In the normal condition of operation of transformer, insulating of conductors coils is with paper and insulating oil. Ignition points of its two components respectively are approximated: 135 °C heat for oil and 200 ⁰C for paper. In the case of a faulty electrical contact, about 10^-3 Ω, at short-circuit; the temperature can reach for a short time, a value of 300 °C-400 °C, which ignite the paper and also the oil. By burning oil, there are local gases that disconnect the power transformer. Securing thermal electrical contacts inside the transformer, in cardboard tube with polyurethane foams, mastik or cement, ensures avoiding gas release and also gas protection working.

Keywords: Power transformer, oil insulatation, electric contacts, gases, gas relay.

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Authors: Youngji Yoo, Cheong-Sool Park, Jun Seok Kim, Young-Hak Lee, Sung-Shick Kim, Jun-Geol Baek

Abstract:

In the semiconductor manufacturing process, large amounts of data are collected from various sensors of multiple facilities. The collected data from sensors have several different characteristics due to variables such as types of products, former processes and recipes. In general, Statistical Quality Control (SQC) methods assume the normality of the data to detect out-of-control states of processes. Although the collected data have different characteristics, using the data as inputs of SQC will increase variations of data, require wide control limits, and decrease performance to detect outof- control. Therefore, it is necessary to separate similar data groups from mixed data for more accurate process control. In the paper, we propose a regression tree using split algorithm based on Pearson distribution to handle non-normal distribution in parametric method. The regression tree finds similar properties of data from different variables. The experiments using real semiconductor manufacturing process data show improved performance in fault detecting ability.

Keywords: Semiconductor, non-normal mixed process data, clustering, Statistical Quality Control (SQC), regression tree, Pearson distribution system.

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Authors: Nour Charara, Iman Jarkass, Maria Sokhn, Elena Mugellini, Omar Abou Khaled

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Intelligent Video-Surveillance (IVS) systems are being more and more popular in security applications. The analysis and recognition of abnormal behaviours in a video sequence has gradually drawn the attention in the field of IVS, since it allows filtering out a large number of useless information, which guarantees the high efficiency in the security protection, and save a lot of human and material resources. We present in this paper ADABeV, an intelligent video-surveillance framework for event recognition in crowded scene to detect the abnormal human behaviour. This framework is attended to be able to achieve real-time alarming, reducing the lags in traditional monitoring systems. This architecture proposal addresses four main challenges: behaviour understanding in crowded scenes, hard lighting conditions, multiple input kinds of sensors and contextual-based adaptability to recognize the active context of the scene.

Keywords: Behavior recognition, Crowded scene, Data fusion, Pattern recognition, Video-surveillance

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Authors: H. B. Kekre, Tanuja K. Sarode

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Mostly transforms are used for speech data compressions which are lossy algorithms. Such algorithms are tolerable for speech data compression since the loss in quality is not perceived by the human ear. However the vector quantization (VQ) has a potential to give more data compression maintaining the same quality. In this paper we propose speech data compression algorithm using vector quantization technique. We have used VQ algorithms LBG, KPE and FCG. The results table shows computational complexity of these three algorithms. Here we have introduced a new performance parameter Average Fractional Change in Speech Sample (AFCSS). Our FCG algorithm gives far better performance considering mean absolute error, AFCSS and complexity as compared to others.

Keywords: Vector Quantization, Data Compression, Encoding, , Speech coding.

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Authors: Eun-Jung Ko, Hyung-Jik Lee, Jeun-Woo Lee

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In ubiqutious healthcare environment, user's health data are transfered to the remote healthcare server by the user's wearable system or mobile phone. These collected user's health data should be managed and analyzed in the healthcare server, so that care giver or user can monitor user's physiological state. In this paper, we designed and developed the intelligent Healthcare Server to manage the user's health data using CDSS and ontology. Our system can analyze user's health data semantically using CDSS and ontology, and report the result of user's physiological raw data to the user and care giver.

Keywords: u-healthcare, CDSS, healthcare server, health data, ontology.

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Authors: Qin Ding, Jim Gasvoda

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Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups have diverse properties. Many heuristic algorithms have been applied to the clustering problem, which is known to be NP Hard. Genetic algorithms have been used in a wide variety of fields to perform clustering, however, the technique normally has a long running time in terms of input set size. This paper proposes an efficient genetic algorithm for clustering on very large data sets, especially on image data sets. The genetic algorithm uses the most time efficient techniques along with preprocessing of the input data set. We test our algorithm on both artificial and real image data sets, both of which are of large size. The experimental results show that our algorithm outperforms the k-means algorithm in terms of running time as well as the quality of the clustering.

Keywords: Clustering, data mining, genetic algorithm, image data.

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Authors: R. Nedunchezhian, K. Anbumani

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A big organization may have multiple branches spread across different locations. Processing of data from these branches becomes a huge task when innumerable transactions take place. Also, branches may be reluctant to forward their data for centralized processing but are ready to pass their association rules. Local mining may also generate a large amount of rules. Further, it is not practically possible for all local data sources to be of the same size. A model is proposed for discovering valid rules from different sized data sources where the valid rules are high weighted rules. These rules can be obtained from the high frequency rules generated from each of the data sources. A data source selection procedure is considered in order to efficiently synthesize rules. Support Equalization is another method proposed which focuses on eliminating low frequency rules at the local sites itself thus reducing the rules by a significant amount.

Keywords: Association rules, multiple data stores, synthesizing, valid rules.

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Authors: Jincheol Han, Hyunsun Ju, Jonghoon Chun

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Sharing consistent and correct master data among disparate applications in a reverse-logistics chain has long been recognized as an intricate problem. Although a master data management (MDM) system can surely assume that responsibility, applications that need to co-operate with it must comply with proprietary query interfaces provided by the specific MDM system. In this paper, we present a RFID-ready MDM system which makes master data readily available for any participating applications in a reverse-logistics chain. We propose a RFID-wrapper as a part of our MDM. It acts as a gateway between any data retrieval request and query interfaces that process it. With the RFID-wrapper, any participating applications in a reverse-logistics chain can easily retrieve master data in a way that is analogous to retrieval of any other RFID-based logistics transactional data.

Keywords: Reverse Logistics, Master Data Management, RFID.

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Authors: Entisar A. Elgmati

Abstract:

Recurrent event data is a special type of multivariate survival data. Dynamic and frailty models are one of the approaches that dealt with this kind of data. A comparison between these two models is studied using the empirical standard deviation of the standardized martingale residual processes as a way of assessing the fit of the two models based on the Aalen additive regression model. Here we found both approaches took heterogeneity into account and produce residual standard deviations close to each other both in the simulation study and in the real data set.

Keywords: Dynamic, frailty, misspecification, recurrent events.

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Authors: Rudainah Alkhazam, Özlem Yaşar Uğurlu

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Immigrant entrepreneurship arises from the start of entrepreneurial activity by immigrants in the country they relocate to. The future prosperity and stability of the refugee-hosting countries depends on the mutual social and economic benefits between the residents and the refugees. Syrian refugees and workers in host countries necessitate efforts to assist their residents and refugees in meeting their daily needs, contributing lawfully to local and possibly regional economies through trade, and instilling hope in their future. This study investigates the effects of Syrian refugee entrepreneurs on host communities' business sectors, focusing on Turkey. Specifically, we examine entrepreneurship in general and its role in the country's economy. Because Turkey is the most popular resettlement destination for Syrian refugees, this study will shed light on the challenges of successful migrant entrepreneurship in Turkey and their role in the business sector. The research relies on a mixed-method approach which helps identify recurring themes, favorable results, and conflicting results across methods, allowing us to draw accurate conclusions. The study will adopt a quantitative method in collecting numerical data from Syrian refugees in Turkey. The self-administered survey would be translated into Arabic to ensure that the respondents understood the questions and possible replies. The research will use survey questionnaires to gather the majority of the data. These surveys would have closed-ended questions with nominal ratio and Likert scales. The data will be analyzed using linear regression and the Statistical Package for Social Sciences (SPSS) to ascertain the role of Syrian entrepreneurs in the business sectors of Turkey. The research will use the findings to make future recommendations. Syrian entrepreneurs, among the migrant entrepreneurs, contribute to the labor market, the majority of whom are young people. This research noted the significant participation of Syrian immigrant women in the entrepreneurship sector. The previous experience of Syrians in the field of trade and running their own business plays a vital role in the success of their business in the host countries. The study shows that Syrian entrepreneurs could integrate effectively into the various Turkish business sectors and could rely on themselves, open and manage their projects, and market them in the Turkish market. Syrian entrepreneurs consider that the investment and labor laws, commercial arrangements, and facilities for obtaining financial resources in Turkey need to be more flexible and available to immigrant entrepreneurs.

Keywords: Entrepreneurship, immigration, Syrian, Turkey, refugees, investors, socio-economic benefits, unemployment.

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Authors: Minsoo Lee, Yoon-kyung Lee, Hyejung Yoon, Soo-kyung Song, Sujeong Cheong

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Data Warehousing tools have become very popular and currently many of them have moved to Web-based user interfaces to make it easier to access and use the tools. The next step is to enable these tools to be used within a portal framework. The portal framework consists of pages having several small windows that contain individual data warehouse query results. There are several issues that need to be considered when designing the architecture for a portal enabled data warehouse query tool. Some issues need special techniques that can overcome the limitations that are imposed by the nature of data warehouse queries. Issues such as single sign-on, query result caching and sharing, customization, scheduling and authorization need to be considered. This paper discusses such issues and suggests an architecture to support data warehouse queries within Web portal frameworks.

Keywords: Data Warehousing tools, data warehousing queries, web portal frameworks.

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Authors: Filiz Ersoz, Deniz Merdin, Taner Ersoz

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The objective of this study provides an insight into enterprises, who need to carry on their sustainability in harmony with the changing competition conditions, technology and laws, regarding the ISO 9001:2015. In the study, ISO 9001:2015, which is planned to be put in force and exists as a draft, was studied and its differences from the previous standard, ISO 9001:2008, were determined. To find out the differences, a survey was conducted among enterprises that implement a quality system. According to the findings obtained at the end of the study, it was observed that the enterprises attach importance to quality and follow the developments about quality management system, and they find the changes in the new draft document necessary.

Keywords: ISO 9001, quality, quality management system, quality revision.

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Authors: M. R. Aghaebrahimi, S. H. Zahiri, M. Amiri

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In this paper a data miner based on the learning automata is proposed and is called LA-miner. The LA-miner extracts classification rules from data sets automatically. The proposed algorithm is established based on the function optimization using learning automata. The experimental results on three benchmarks indicate that the performance of the proposed LA-miner is comparable with (sometimes better than) the Ant-miner (a data miner algorithm based on the Ant Colony optimization algorithm) and CNZ (a well-known data mining algorithm for classification).

Keywords: Data mining, Learning automata, Classification rules, Knowledge discovery.

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Authors: A. Krishna Veni, R.Geetha

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Wireless Sensor Networks (WSNs) are suitable for many scenarios in the real world. The retrieval of data is made efficient by the data aggregation techniques. Many techniques for the data aggregation are offered and most of the existing schemes are not energy efficient and secure. However, the existing techniques use the traditional clustering approach where there is a delay during the packet transmission since there is no proper scheduling. The presented system uses the Velocity Energy-efficient and Link-aware Cluster-Tree (VELCT) scheme in which there is a Data Collection Tree (DCT) which improves the lifetime of the network. The VELCT scheme and the construction of DCT reduce the delay and traffic. The network lifetime can be increased by avoiding the frequent change in cluster topology. Secure and Efficient Transmission of Aggregated data (SETA) improves the security of the data transmission via the trust value of the nodes prior the aggregation of data. Since SETA considers the data only from the trustworthy nodes for aggregation, it is more secure in transmitting the data thereby improving the accuracy of aggregated data.

Keywords: Aggregation, lifetime, network security, wireless sensor network.

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Authors: M. Amir Abas, M. Dahlui

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This paper presents the development of analysis tools for Home Agriculture project. The tools are required for monitoring the condition of greenhouse which involves two components: measurement hardware and data analysis engine. Measurement hardware is functioned to measure environment parameters such as temperature, humidity, air quality, dust and etc while analysis tool is used to analyse and interpret the integrated data against the condition of weather, quality of health, irradiance, quality of soil and etc. The current development of the tools is completed for off-line data recorded technique. The data is saved in MMC and transferred via ZigBee to Environment Data Manager (EDM) for data analysis. EDM converts the raw data and plot three combination graphs. It has been applied in monitoring three months data measurement for irradiance, temperature and humidity of the greenhouse..

Keywords: Monitoring, Environment, Greenhouse, Analysis tools

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Authors: Emad T. Khalaf, Norrozila Sulaiman

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Many researchers are working on information hiding techniques using different ideas and areas to hide their secrete data. This paper introduces a robust technique of hiding secret data in image based on LSB insertion and RSA encryption technique. The key of the proposed technique is to encrypt the secret data. Then the encrypted data will be converted into a bit stream and divided it into number of segments. However, the cover image will also be divided into the same number of segments. Each segment of data will be compared with each segment of image to find the best match segment, in order to create a new random sequence of segments to be inserted then in a cover image. Experimental results show that the proposed technique has a high security level and produced better stego-image quality.

Keywords: steganography; LSB Matching; RSA Encryption; data segments

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Authors: S. Sarumathi, N. Shanthi

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Due to the fast and flawless technological innovation there is a tremendous amount of data dumping all over the world in every domain such as Pattern Recognition, Machine Learning, Spatial Data Mining, Image Analysis, Fraudulent Analysis, World Wide Web etc., This issue turns to be more essential for developing several tools for data mining functionalities. The major aim of this paper is to analyze various tools which are used to build a resourceful analytical or descriptive model for handling large amount of information more efficiently and user friendly. In this survey the diverse tools are illustrated with their extensive technical paradigm, outstanding graphical interface and inbuilt multipath algorithms in which it is very useful for handling significant amount of data more indeed.

Keywords: Classification, Clustering, Data Mining, Machine learning, Visualization.

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Authors: M. Benachour, N. Benachour

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Stainless steel has been employed in many engineering applications ranging from pharmaceutical equipment to piping in the nuclear reactors and storage to chemical products. In this attempt, simulation of fatigue crack growth based on experimental results of austenitic stainless steel 304L was presented using AFGROW code when NASGRO mode laws adopted. Double through crack at hole specimen is used in this investigation under constant amplitude loading. Effect of mean stress is highlighted. Results show that fatigue crack growth rate (FCGR) and fatigue life were affected by maximum applied load and dimension of hole. An equivalent of Paris law for this material was estimated.

Keywords: Fatigue crack, stainless steel, mean stress, amplitudeloading.

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Authors: Shigeaki Sakurai, Makino Kyoko, Shigeru Matsumoto

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This paper proposes a method that predicts attractive evaluation objects. In the learning phase, the method inductively acquires trend rules from complex sequential data. The data is composed of two types of data. One is numerical sequential data. Each evaluation object has respective numerical sequential data. The other is text sequential data. Each evaluation object is described in texts. The trend rules represent changes of numerical values related to evaluation objects. In the prediction phase, the method applies new text sequential data to the trend rules and evaluates which evaluation objects are attractive. This paper verifies the effect of the proposed method by using stock price sequences and news headline sequences. In these sequences, each stock brand corresponds to an evaluation object. This paper discusses validity of predicted attractive evaluation objects, the process time of each phase, and the possibility of application tasks.

Keywords: Trend rule, frequent pattern, numerical sequential data, text sequential data, evaluation object.

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Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: Genetic data, Pinzgau cattle, supervised learning.

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Authors: Azhar Rauf, Sareer Badshah, Shah Khusro

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This paper analyzes different techniques of the fine grained security of relational databases for the two variables-data accessibility and inference. Data accessibility measures the amount of data available to the users after applying a security technique on a table. Inference is the proportion of information leakage after suppressing a cell containing secret data. A row containing a secret cell which is suppressed can become a security threat if an intruder generates useful information from the related visible information of the same row. This paper measures data accessibility and inference associated with row, cell, and column level security techniques. Cell level security offers greatest data accessibility as it suppresses secret data only. But on the other hand, there is a high probability of inference in cell level security. Row and column level security techniques have least data accessibility and inference. This paper introduces cell plus innocent security technique that utilizes the cell level security method but suppresses some innocent data to dodge an intruder that a suppressed cell may not necessarily contain secret data. Four variations of the technique namely cell plus innocent 1/4, cell plus innocent 2/4, cell plus innocent 3/4, and cell plus innocent 4/4 respectively have been introduced to suppress innocent data equal to 1/4, 2/4, 3/4, and 4/4 percent of the true secret data inside the database. Results show that the new technique offers better control over data accessibility and inference as compared to the state-of-theart security techniques. This paper further discusses the combination of techniques together to be used. The paper shows that cell plus innocent 1/4, 2/4, and 3/4 techniques can be used as a replacement for the cell level security.

Keywords: Fine Grained Security, Data Accessibility, Inference, Row, Cell, Column Level Security.

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Authors: Sujala.D.Shetty, S.Vadivel, Sakshi Vaghella

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Data mining is the process of sifting through large volumes of data, analyzing data from different perspectives and summarizing it into useful information. One of the widely used desktop applications for data mining is the Weka tool which is nothing but a collection of machine learning algorithms implemented in Java and open sourced under the General Public License (GPL). A web service is a software system designed to support interoperable machine to machine interaction over a network using SOAP messages. Unlike a desktop application, a web service is easy to upgrade, deliver and access and does not occupy any memory on the system. Keeping in mind the advantages of a web service over a desktop application, in this paper we are demonstrating how this Java based desktop data mining application can be implemented as a web service to support data mining across the internet.

Keywords: desktop application, Weka mining, web service

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: Local nonlinear estimation, LWPR algorithm, Online training method.

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Authors: Julius Onyancha, Valentina Plekhanova

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One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.

Keywords: Web log data, web user profile, user interest, noise web data learning, machine learning.

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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: Nittaya Kerdprasop, Kittisak Kerdprasop

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Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropriate models and interpret the discovered knowledge. In this paper, we describe a framework of an intelligent and complete data mining system called SUT-Miner. Our system is comprised of a full complement of major DM algorithms, pre-DM and post-DM functionalities. It is the post-DM packages that ease the DM deployment for business intelligence applications.

Keywords: Business intelligence, data mining, functionalprogramming, intelligent system.

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Authors: S. Sarumathi, N. Shanthi, M. Sharmila

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Clustering in data mining is an unsupervised learning technique of aggregating the data objects into meaningful groups such that the intra cluster similarity of objects are maximized and inter cluster similarity of objects are minimized. Over the past decades several clustering tools were emerged in which clustering algorithms are inbuilt and are easier to use and extract the expected results. Data mining mainly deals with the huge databases that inflicts on cluster analysis and additional rigorous computational constraints. These challenges pave the way for the emergence of powerful expansive data mining clustering softwares. In this survey, a variety of clustering tools used in data mining are elucidated along with the pros and cons of each software.

Keywords: Cluster Analysis, Clustering Algorithms, Clustering Techniques, Association, Visualization.

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Authors: Kittisak Kerdprasop, Nittaya Kerdprasop, Pairote Sattayatham

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Data stream analysis is the process of computing various summaries and derived values from large amounts of data which are continuously generated at a rapid rate. The nature of a stream does not allow a revisit on each data element. Furthermore, data processing must be fast to produce timely analysis results. These requirements impose constraints on the design of the algorithms to balance correctness against timely responses. Several techniques have been proposed over the past few years to address these challenges. These techniques can be categorized as either dataoriented or task-oriented. The data-oriented approach analyzes a subset of data or a smaller transformed representation, whereas taskoriented scheme solves the problem directly via approximation techniques. We propose a hybrid approach to tackle the data stream analysis problem. The data stream has been both statistically transformed to a smaller size and computationally approximated its characteristics. We adopt a Monte Carlo method in the approximation step. The data reduction has been performed horizontally and vertically through our EMR sampling method. The proposed method is analyzed by a series of experiments. We apply our algorithm on clustering and classification tasks to evaluate the utility of our approach.

Keywords: Data Stream, Monte Carlo, Sampling, DensityEstimation.

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Authors: Paola Gattinoni, Laura Scesi

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According to the European laws, there is the possibility of reusing the washing wastes for the environmental requalification of quarry lakes. The paper deals with the hydrogeological aspects involved in this possibility, as the introduction of finest wastes in the quarry lakes can generate alterations of the hydrogeological setting of the area, and problems for the future accessibility of the zone. To evaluate the hydrogeological compatibility of the washing wastes reuse in quarry lakes a groundwater numerical model was carried out, pointing out both the hydrogeological feasibility of this intervention and some guide lines for its optimization, in terms of inflow point with regard the groundwater flow direction and loss of volume in the quarry lake.

Keywords: Groundwater numerical modeling, hydrogeologicalalteration, quarry lake, silty-clay wastes.

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