Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 191

Search results for: Rule Discovery

191 A Hybrid Approach for Quantification of Novelty in Rule Discovery

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

Abstract:

Rule Discovery is an important technique for mining knowledge from large databases. Use of objective measures for discovering interesting rules lead 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 that uses objective and subjective measures to quantify novelty of the discovered rules in terms of their deviations from the known rules. 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 quite promising.

Keywords: Knowledge Discovery in Databases (KDD), Data Mining, Rule Discovery, Interestingness, Subjective Measures, Novelty Measure.

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190 Genetic Programming Approach to Hierarchical Production Rule Discovery

Authors: Basheer M. Al-Maqaleh, Kamal K. Bharadwaj

Abstract:

Automated discovery of hierarchical structures in large data sets has been an active research area in the recent past. This paper focuses on the issue of mining generalized rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses flat rules as initial individuals of GP and discovers hierarchical structure. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy. Experimental results are presented to demonstrate the performance of the proposed algorithm.

Keywords: Genetic Programming, Hierarchy, Knowledge Discovery in Database, Subsumption Matrix.

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189 Web-Based Cognitive Writing Instruction (WeCWI): A Hybrid e-Framework for Instructional Design

Authors: Boon Yih Mah

Abstract:

Web-based Cognitive Writing Instruction (WeCWI) is a hybrid e-framework for the development of a web-based instruction (WBI), which contributes towards instructional design and language development. WeCWI divides its contribution in instructional design into macro and micro perspectives. In macro perspective, being a 21st century educator by disseminating knowledge and sharing ideas with the in-class and global learners is initiated. By leveraging the virtue of technology, WeCWI aims to transform an educator into an aggregator, curator, publisher, social networker and ultimately, a web-based instructor. Since the most notable contribution of integrating technology is being a tool of teaching as well as a stimulus for learning, WeCWI focuses on the use of contemporary web tools based on the multiple roles played by the 21st century educator. The micro perspective in instructional design draws attention to the pedagogical approaches focusing on three main aspects: reading, discussion, and writing. With the effective use of pedagogical approaches through free reading and enterprises, technology adds new dimensions and expands the boundaries of learning capacity. Lastly, WeCWI also imparts the fundamental theories and models for web-based instructors’ awareness such as interactionist theory, cognitive information processing (CIP) theory, computer-mediated communication (CMC), e-learning interactionalbased model, inquiry models, sensory mind model, and leaning styles model.

Keywords: WeCWI, instructional discovery, technological discovery, pedagogical discovery, theoretical discovery.

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188 Web-Based Cognitive Writing Instruction (WeCWI): A Theoretical-and-Pedagogical e-Framework for Language Development

Authors: Boon Yih Mah

Abstract:

Web-based Cognitive Writing Instruction (WeCWI)’s contribution towards language development can be divided into linguistic and non-linguistic perspectives. In linguistic perspective, WeCWI focuses on the literacy and language discoveries, while the cognitive and psychological discoveries are the hubs in non-linguistic perspective. In linguistic perspective, WeCWI draws attention to free reading and enterprises, which are supported by the language acquisition theories. Besides, the adoption of process genre approach as a hybrid guided writing approach fosters literacy development. Literacy and language developments are interconnected in the communication process; hence, WeCWI encourages meaningful discussion based on the interactionist theory that involves input, negotiation, output, and interactional feedback. Rooted in the elearning interaction-based model, WeCWI promotes online discussion via synchronous and asynchronous communications, which allows interactions happened among the learners, instructor, and digital content. In non-linguistic perspective, WeCWI highlights on the contribution of reading, discussion, and writing towards cognitive development. Based on the inquiry models, learners’ critical thinking is fostered during information exploration process through interaction and questioning. Lastly, to lower writing anxiety, WeCWI develops the instructional tool with supportive features to facilitate the writing process. To bring a positive user experience to the learner, WeCWI aims to create the instructional tool with different interface designs based on two different types of perceptual learning style.

Keywords: WeCWI, literacy discovery, language discovery, cognitive discovery, psychological discovery.

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187 Evolutionary Approach for Automated Discovery of Censored Production Rules

Authors: Kamal K. Bharadwaj, Basheer M. Al-Maqaleh

Abstract:

In the recent past, there has been an increasing interest in applying evolutionary methods to Knowledge Discovery in Databases (KDD) and a number of successful applications of Genetic Algorithms (GA) and Genetic Programming (GP) to KDD have been demonstrated. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski & Winston 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 and changes the polarity of D to ~D. This paper presents a classification algorithm based on evolutionary approach that discovers comprehensible rules with exceptions in the form of CPRs. The proposed approach has flexible chromosome encoding, where each chromosome corresponds to a CPR. Appropriate genetic operators are suggested and a fitness function is proposed that incorporates the basic constraints on CPRs. Experimental results are presented to demonstrate the performance of the proposed algorithm.

Keywords: Censored Production Rule, Data Mining, MachineLearning, Evolutionary Algorithms.

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186 Generating Speq Rules based on Automatic Proof of Logical Equivalence

Authors: Katsunori Miura, Kiyoshi Akama, Hiroshi Mabuchi

Abstract:

In the Equivalent Transformation (ET) computation model, a program is constructed by the successive accumulation of ET rules. A method by meta-computation by which a correct ET rule is generated has been proposed. Although the method covers a broad range in the generation of ET rules, all important ET rules are not necessarily generated. Generation of more ET rules can be achieved by supplementing generation methods which are specialized for important ET rules. A Specialization-by-Equation (Speq) rule is one of those important rules. A Speq rule describes a procedure in which two variables included in an atom conjunction are equalized due to predicate constraints. In this paper, we propose an algorithm that systematically and recursively generate Speq rules and discuss its effectiveness in the synthesis of ET programs. A Speq rule is generated based on proof of a logical formula consisting of given atom set and dis-equality. The proof is carried out by utilizing some ET rules and the ultimately obtained rules in generating Speq rules.

Keywords: Equivalent transformation, ET rule, Equation of two variables, Rule generation, Specialization-by-Equation rule

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185 Model Discovery and Validation for the Qsar Problem using Association Rule Mining

Authors: Luminita Dumitriu, Cristina Segal, Marian Craciun, Adina Cocu, Lucian P. Georgescu

Abstract:

There are several approaches in trying to solve the Quantitative 1Structure-Activity Relationship (QSAR) problem. These approaches are based either on statistical methods or on predictive data mining. Among the statistical methods, one should consider regression analysis, pattern recognition (such as cluster analysis, factor analysis and principal components analysis) or partial least squares. Predictive data mining techniques use either neural networks, or genetic programming, or neuro-fuzzy knowledge. These approaches have a low explanatory capability or non at all. This paper attempts to establish a new approach in solving QSAR problems using descriptive data mining. This way, the relationship between the chemical properties and the activity of a substance would be comprehensibly modeled.

Keywords: association rules, classification, data mining, Quantitative Structure - Activity Relationship.

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184 Learning Classifier Systems Approach for Automated Discovery of Censored Production Rules

Authors: Suraiya Jabin, Kamal K. Bharadwaj

Abstract:

In the recent past Learning Classifier Systems have been successfully used for data mining. Learning Classifier System (LCS) is basically a machine learning technique which combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. All LCSs models more or less, comprise four main components; a finite population of condition–action rules, called classifiers; the performance component, which governs the interaction with the environment; the credit assignment component, which distributes the reward received from the environment to the classifiers accountable for the rewards obtained; the discovery component, which is responsible for discovering better rules and improving existing ones through a genetic algorithm. The concatenate of the production rules in the LCS form the genotype, and therefore the GA should operate on a population of classifier systems. This approach is known as the 'Pittsburgh' Classifier Systems. Other LCS that perform their GA at the rule level within a population are known as 'Mitchigan' Classifier Systems. The most predominant representation of the discovered knowledge is the standard production rules (PRs) in the form of IF P THEN D. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski and Winston 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 Censor C is an exception to the rule. Such rules are employed in situations, in which 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 CPR expresses important information, while the UNLESS C part acts only as a switch and changes the polarity of D to ~D. In this paper Pittsburgh style LCSs approach is used for automated discovery of CPRs. An appropriate encoding scheme is suggested to represent a chromosome consisting of fixed size set of CPRs. Suitable genetic operators are designed for the set of CPRs and individual CPRs and also appropriate fitness function is proposed that incorporates basic constraints on CPR. Experimental results are presented to demonstrate the performance of the proposed learning classifier system.

Keywords: Censored Production Rule, Data Mining, GeneticAlgorithm, Learning Classifier System, Machine Learning, PittsburgApproach, , Reinforcement learning.

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183 Models to Customise Web Service Discovery Result using Static and Dynamic Parameters

Authors: Kee-Leong Tan, Cheng-Suan Lee, Hui-Na Chua

Abstract:

This paper presents three models which enable the customisation of Universal Description, Discovery and Integration (UDDI) query results, based on some pre-defined and/or real-time changing parameters. These proposed models detail the requirements, design and techniques which make ranking of Web service discovery results from a service registry possible. Our contribution is two fold: First, we present an extension to the UDDI inquiry capabilities. This enables a private UDDI registry owner to customise or rank the query results, based on its business requirements. Second, our proposal utilises existing technologies and standards which require minimal changes to existing UDDI interfaces or its data structures. We believe these models will serve as valuable reference for enhancing the service discovery methods within a private UDDI registry environment.

Keywords: Web service, discovery, semantic, SOA, registry, UDDI.

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182 Three Computational Mathematics Techniques: Comparative Determination of Area under Curve

Authors: Khalid Pervaiz Akhter, Mahmood Ahmad, Ghulam Murtaza, Ishrat Shafi, Zafar Javed

Abstract:

The objective of this manuscript is to find area under the plasma concentration- time curve (AUC) for multiple doses of salbutamol sulphate sustained release tablets (Ventolin® oral tablets SR 8 mg, GSK, Pakistan) in the group of 18 healthy adults by using computational mathematics techniques. Following the administration of 4 doses of Ventolin® tablets 12 hourly to 24 healthy human subjects and bioanalysis of obtained plasma samples, plasma drug concentration-time profile was constructed. AUC, an important pharmacokinetic parameter, was measured using integrated equation of multiple oral dose regimens. The approximated AUC was also calculated by using computational mathematics techniques such as repeated rectangular, repeated trapezium and repeated Simpson's rule and compared with exact value of AUC calculated by using integrated equation of multiple oral dose regimens to find best computational mathematics method that gives AUC values closest to exact. The exact values of AUC for four consecutive doses of Ventolin® oral tablets were 150.5819473, 157.8131756, 164.4178231 and 162.78 ng.h/ml while the closest values approximated AUC values were 149.245962, 157.336171, 164.2585768 and 162.289224 ng.h/ml, respectively as found by repeated rectangular rule. The errors in the approximated values of AUC were negligible. It is concluded that all computational tools approximated values of AUC accurately but the repeated rectangular rule gives slightly better approximated values of AUC as compared to repeated trapezium and repeated Simpson's rules.

Keywords: Salbutamol sulphate, Area under curve (AUC), repeated rectangular rule, repeated trapezium rule, repeated Simpson's rule.

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181 Effective Keyword and Similarity Thresholds for the Discovery of Themes from the User Web Access Patterns

Authors: Haider A Ramadhan, Khalil Shihab

Abstract:

Clustering techniques have been used by many intelligent software agents to group similar access patterns of the Web users into high level themes which express users intentions and interests. However, such techniques have been mostly focusing on one salient feature of the Web document visited by the user, namely the extracted keywords. The major aim of these techniques is to come up with an optimal threshold for the number of keywords needed to produce more focused themes. In this paper we focus on both keyword and similarity thresholds to generate themes with concentrated themes, and hence build a more sound model of the user behavior. The purpose of this paper is two fold: use distance based clustering methods to recognize overall themes from the Proxy log file, and suggest an efficient cut off levels for the keyword and similarity thresholds which tend to produce more optimal clusters with better focus and efficient size.

Keywords: Data mining, knowledge discovery, clustering, dataanalysis, Web log analysis, theme based searching.

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180 A Tree Based Association Rule Approach for XML Data with Semantic Integration

Authors: D. Sasikala, K. Premalatha

Abstract:

The use of eXtensible Markup Language (XML) in web, business and scientific databases lead to the development of methods, techniques and systems to manage and analyze XML data. Semi-structured documents suffer due to its heterogeneity and dimensionality. XML structure and content mining represent convergence for research in semi-structured data and text mining. As the information available on the internet grows drastically, extracting knowledge from XML documents becomes a harder task. Certainly, documents are often so large that the data set returned as answer to a query may also be very big to convey the required information. To improve the query answering, a Semantic Tree Based Association Rule (STAR) mining method is proposed. This method provides intentional information by considering the structure, content and the semantics of the content. The method is applied on Reuter’s dataset and the results show that the proposed method outperforms well.

Keywords: Semi--structured Document, Tree based Association Rule (TAR), Semantic Association Rule Mining.

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179 Resource Discovery in Web-Services Based Grids

Authors: Damandeep Kaur, Jyotsna Sengupta

Abstract:

A Web-services based grid infrastructure is evolving to be readily available in the near future. In this approach, the Web services are inherited (encapsulated or functioned) into the same existing Grid services class. In practice there is not much difference between the existing Web and grid infrastructure. Grid services emerged as stateful web services. In this paper, we present the key components of web-services based grid and also how the resource discovery is performed on web-services based grid considering resource discovery, as a critical service, to be provided by any type of grid.

Keywords: Web services, resource discovery, grid computing, OGSA.

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178 Association Rule and Decision Tree based Methodsfor Fuzzy Rule Base Generation

Authors: Ferenc Peter Pach, Janos Abonyi

Abstract:

This paper focuses on the data-driven generation of fuzzy IF...THEN rules. The resulted fuzzy rule base can be applied to build a classifier, a model used for prediction, or it can be applied to form a decision support system. Among the wide range of possible approaches, the decision tree and the association rule based algorithms are overviewed, and two new approaches are presented based on the a priori fuzzy clustering based partitioning of the continuous input variables. An application study is also presented, where the developed methods are tested on the well known Wisconsin Breast Cancer classification problem.

Keywords:

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177 Learning Classifier Systems Approach for Automated Discovery of Crisp and Fuzzy Hierarchical Production Rules

Authors: Suraiya Jabin, Kamal K. Bharadwaj

Abstract:

This research presents a system for post processing of data that takes mined flat rules as input and discovers crisp as well as fuzzy hierarchical structures using Learning Classifier System approach. Learning Classifier System (LCS) is basically a machine learning technique that combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. Crisp description for a concept usually cannot represent human knowledge completely and practically. In the proposed Learning Classifier System initial population is constructed as a random collection of HPR–trees (related production rules) and crisp / fuzzy hierarchies are evolved. A fuzzy subsumption relation is suggested for the proposed system and based on Subsumption Matrix (SM), a suitable fitness function is proposed. Suitable genetic operators are proposed for the chosen chromosome representation method. For implementing reinforcement a suitable reward and punishment scheme is also proposed. Experimental results are presented to demonstrate the performance of the proposed system.

Keywords: Hierarchical Production Rule, Data Mining, Learning Classifier System, Fuzzy Subsumption Relation, Subsumption matrix, Reinforcement Learning.

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176 Discovery of Quantified Hierarchical Production Rules from Large Set of Discovered Rules

Authors: Tamanna Siddiqui, M. Afshar Alam

Abstract:

Automated discovery of Rule is, due to its applicability, one of the most fundamental and important method in KDD. It has been an active research area in the recent past. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form: Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. This paper focuses on the issue of mining Quantified rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses Quantified production rules as initial individuals of GP and discovers hierarchical structure. In proposed approach rules are quantified by using Dempster Shafer theory. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Quantified Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy, using Dempster Shafer theory. Experimental results are presented to demonstrate the performance of the proposed algorithm.

Keywords: Knowledge discovery in database, quantification, dempster shafer theory, genetic programming, hierarchy, subsumption matrix.

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175 Fuzzy Rules Generation and Extraction from Support Vector Machine Based on Kernel Function Firing Signals

Authors: Prasan Pitiranggon, Nunthika Benjathepanun, Somsri Banditvilai, Veera Boonjing

Abstract:

Our study proposes an alternative method in building Fuzzy Rule-Based System (FRB) from Support Vector Machine (SVM). The first set of fuzzy IF-THEN rules is obtained through an equivalence of the SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS). The second set of rules is generated by combining the first set based on strength of firing signals of support vectors using Gaussian kernel. The final set of rules is then obtained from the second set through input scatter partitioning. A distinctive advantage of our method is the guarantee that the number of final fuzzy IFTHEN rules is not more than the number of support vectors in the trained SVM. The final FRB system obtained is capable of performing classification with results comparable to its SVM counterpart, but it has an advantage over the black-boxed SVM in that it may reveal human comprehensible patterns.

Keywords: Fuzzy Rule Base, Rule Extraction, Rule Generation, Support Vector Machine.

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174 A Framework for Scalable Autonomous P2P Resource Discovery for the Grid Implementation

Authors: Hesham A. Ali, Mofreh M. Salem, Ahmed A. Hamza

Abstract:

Recently, there have been considerable efforts towards the convergence between P2P and Grid computing in order to reach a solution that takes the best of both worlds by exploiting the advantages that each offers. Augmenting the peer-to-peer model to the services of the Grid promises to eliminate bottlenecks and ensure greater scalability, availability, and fault-tolerance. The Grid Information Service (GIS) directly influences quality of service for grid platforms. Most of the proposed solutions for decentralizing the GIS are based on completely flat overlays. The main contributions for this paper are: the investigation of a novel resource discovery framework for Grid implementations based on a hierarchy of structured peer-to-peer overlay networks, and introducing a discovery algorithm utilizing the proposed framework. Validation of the framework-s performance is done via simulation. Experimental results show that the proposed organization has the advantage of being scalable while providing fault-isolation, effective bandwidth utilization, and hierarchical access control. In addition, it will lead to a reliable, guaranteed sub-linear search which returns results within a bounded interval of time and with a smaller amount of generated traffic within each domain.

Keywords: Grid computing, grid information service, P2P, resource discovery.

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173 Pruning Algorithm for the Minimum Rule Reduct Generation

Authors: Şahin Emrah Amrahov, Fatih Aybar, Serhat Doğan

Abstract:

In this paper we consider the rule reduct generation problem. Rule Reduct Generation (RG) and Modified Rule Generation (MRG) algorithms, that are used to solve this problem, are well-known. Alternative to these algorithms, we develop Pruning Rule Generation (PRG) algorithm. We compare the PRG algorithm with RG and MRG.

Keywords: Rough sets, Decision rules, Rule induction, Classification.

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172 Novelty as a Measure of Interestingness in Knowledge Discovery

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

Abstract:

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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171 Fortification for P2P Grid Computing Used for Resource Discovery

Authors: Bhawneet Singh Marwah, Rishabh Rastogi, Shinon Kochar

Abstract:

Grid computing provides an effective infrastructure for massive computation among flexible and dynamic collection of individual system for resource discovery. The major challenge for grid computing is to prevent breaches and secure the data from trespassers. To overcome such conflicts a semantic approach can be designed which will filter the access requests of peers by checking the resource description specifying the data and the metadata as factual statements. Between every node in the grid a semantic firewall as a middleware will be present The intruder will be required to present an application specifying there needs to the firewall and hence accordingly the system will grant or deny the application request.

Keywords: Grid Computing, Metadata, Semantic, Peers, Resource Discovery, Firewall.

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170 Intrapreneurship Discovery: Standard Strategy to Boost Innovation inside Companies

Authors: Chiara Mansanta, Daniela Sani

Abstract:

This paper studies the concept of intrapreneurship discovery for innovation and technology development related to the manufacturing industries set up in the center of Italy, in Marche Region. The study underlined the key drivers of the innovation process and the main factors that influence innovation. Starting from a literature study on open innovation, this paper examines the role of human capital to support company’s development. The empirical part of the study is based on a survey to 151 manufacturing companies that represent the 34% of that universe at the regional level. The survey underlined the main KPI’s that influence companies in their decision processes; then tools for these decision processes are presented.

Keywords: Business model, decision making, intrapreneurship discovery, open innovation, standard methodology.

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169 FCA-based Conceptual Knowledge Discovery in Folksonomy

Authors: Yu-Kyung Kang, Suk-Hyung Hwang, Kyoung-Mo Yang

Abstract:

The tagging data of (users, tags and resources) constitutes a folksonomy that is the user-driven and bottom-up approach to organizing and classifying information on the Web. Tagging data stored in the folksonomy include a lot of very useful information and knowledge. However, appropriate approach for analyzing tagging data and discovering hidden knowledge from them still remains one of the main problems on the folksonomy mining researches. In this paper, we have proposed a folksonomy data mining approach based on FCA for discovering hidden knowledge easily from folksonomy. Also we have demonstrated how our proposed approach can be applied in the collaborative tagging system through our experiment. Our proposed approach can be applied to some interesting areas such as social network analysis, semantic web mining and so on.

Keywords: Folksonomy data mining, formal concept analysis, collaborative tagging, conceptual knowledge discovery, classification.

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168 Eclectic Rule-Extraction from Support Vector Machines

Authors: Nahla Barakat, Joachim Diederich

Abstract:

Support vector machines (SVMs) have shown superior performance compared to other machine learning techniques, especially in classification problems. Yet one limitation of SVMs is the lack of an explanation capability which is crucial in some applications, e.g. in the medical and security domains. In this paper, a novel approach for eclectic rule-extraction from support vector machines is presented. This approach utilizes the knowledge acquired by the SVM and represented in its support vectors as well as the parameters associated with them. The approach includes three stages; training, propositional rule-extraction and rule quality evaluation. Results from four different experiments have demonstrated the value of the approach for extracting comprehensible rules of high accuracy and fidelity.

Keywords: Data mining, hybrid rule-extraction algorithms, medical diagnosis, SVMs

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167 Learning Algorithms for Fuzzy Inference Systems Composed of Double- and Single-Input Rule Modules

Authors: Hirofumi Miyajima, Kazuya Kishida, Noritaka Shigei, Hiromi Miyajima

Abstract:

Most of self-tuning fuzzy systems, which are automatically constructed from learning data, are based on the steepest descent method (SDM). However, this approach often requires a large convergence time and gets stuck into a shallow local minimum. One of its solutions is to use fuzzy rule modules with a small number of inputs such as DIRMs (Double-Input Rule Modules) and SIRMs (Single-Input Rule Modules). In this paper, we consider a (generalized) DIRMs model composed of double and single-input rule modules. Further, in order to reduce the redundant modules for the (generalized) DIRMs model, pruning and generative learning algorithms for the model are suggested. In order to show the effectiveness of them, numerical simulations for function approximation, Box-Jenkins and obstacle avoidance problems are performed.

Keywords: Box-Jenkins’s problem, Double-input rule module, Fuzzy inference model, Obstacle avoidance, Single-input rule module.

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166 Automata Theory Approach for Solving Frequent Pattern Discovery Problems

Authors: Renáta Iváncsy, István Vajk

Abstract:

The various types of frequent pattern discovery problem, namely, the frequent itemset, sequence and graph mining problems are solved in different ways which are, however, in certain aspects similar. The main approach of discovering such patterns can be classified into two main classes, namely, in the class of the levelwise methods and in that of the database projection-based methods. The level-wise algorithms use in general clever indexing structures for discovering the patterns. In this paper a new approach is proposed for discovering frequent sequences and tree-like patterns efficiently that is based on the level-wise issue. Because the level-wise algorithms spend a lot of time for the subpattern testing problem, the new approach introduces the idea of using automaton theory to solve this problem.

Keywords: Frequent pattern discovery, graph mining, pushdownautomaton, sequence mining, state machine, tree mining.

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165 Knowledge Discovery Techniques for Talent Forecasting in Human Resource Application

Authors: Hamidah Jantan, Abdul Razak Hamdan, Zulaiha Ali Othman

Abstract:

Human Resource (HR) applications can be used to provide fair and consistent decisions, and to improve the effectiveness of decision making processes. Besides that, among the challenge for HR professionals is to manage organization talents, especially to ensure the right person for the right job at the right time. For that reason, in this article, we attempt to describe the potential to implement one of the talent management tasks i.e. identifying existing talent by predicting their performance as one of HR application for talent management. This study suggests the potential HR system architecture for talent forecasting by using past experience knowledge known as Knowledge Discovery in Database (KDD) or Data Mining. This article consists of three main parts; the first part deals with the overview of HR applications, the prediction techniques and application, the general view of Data mining and the basic concept of talent management in HRM. The second part is to understand the use of Data Mining technique in order to solve one of the talent management tasks, and the third part is to propose the potential HR system architecture for talent forecasting.

Keywords: HR Application, Knowledge Discovery inDatabase (KDD), Talent Forecasting.

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164 Application of Data Mining Techniques for Tourism Knowledge Discovery

Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

Abstract:

Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: Classification algorithms; data mining; tourism; knowledge discovery.

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163 Proffering a Brand New Methodology to Resource Discovery in Grid based on Economic Criteria Using Learning Automata

Authors: Ali Sarhadi, Mohammad Reza Meybodi, Ali Yousefi

Abstract:

Resource discovery is one of the chief services of a grid. A new approach to discover the provenances in grid through learning automata has been propounded in this article. The objective of the aforementioned resource-discovery service is to select the resource based upon the user-s applications and the mercantile yardsticks that is to say opting for an originator which can accomplish the user-s tasks in the most economic manner. This novel service is submitted in two phases. We proffered an applicationbased categorization by means of an intelligent nerve-prone plexus. The user in question sets his or her application as the input vector of the nerve-prone nexus. The output vector of the aforesaid network limns the appropriateness of any one of the resource for the presented executive procedure. The most scrimping option out of those put forward in the previous stage which can be coped with to fulfill the task in question is picked out. Te resource choice is carried out by means of the presented algorithm based upon the learning automata.

Keywords: Resource discovery, learning automata, neural network, economic policy

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162 An Intelligent Approach of Rough Set in Knowledge Discovery Databases

Authors: Hrudaya Ku. Tripathy, B. K. Tripathy, Pradip K. Das

Abstract:

Knowledge Discovery in Databases (KDD) has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) interesting and previously unknown knowledge from very large real-world databases. Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge-based systems. One important concept related to RST is that of a rough relation. In this paper we presented the current status of research on applying rough set theory to KDD, which will be helpful for handle the characteristics of real-world databases. The main aim is to show how rough set and rough set analysis can be effectively used to extract knowledge from large databases.

Keywords: Data mining, Data tables, Knowledge discovery in database (KDD), Rough sets.

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