Web Mining : a Synergic Approach Resorting to Classifications and Clustering.

Front Cover -- Half Title -- RIVER PUBLISHERS SERIES IN INFORMATION SCIENCE AND TECHNOLOGY -- Title Page - Web Mining: A Synergic Approach Resorting to Classifications and Clustering -- Copyright Page -- Contents -- Preface -- Acknowledgment -- List of Figures -- List of Tables -- List of Graphs -- List of Abbreviations -- Chapter 1 - Introduction -- 1.1 Basic Notion of Data Mining -- 1.2 Knowledge Discovery:The Very Rationale Behind Data Mining -- 1.3 Challenges in the Development of Data Mining -- 1.3.1 Scalability -- 1.3.2 High Dimensionality -- 1.3.3 Heterogeneous and Complex Data -- 1.3.4 Data Ownership and Distribution -- 1.3.5 Non-Traditional Analysis -- 1.4 Importance of Data Mining -- 1.5 Classification of Data Mining Systems -- 1.5.1 The Databases Mined -- 1.5.2 The Knowledge Mined -- 1.5.3 The Techniques Utilized -- 1.5.4 The Application Adopted -- 1.6 Generic Architecture of Data Mining System -- 1.7 Major Issues in Data Mining -- 1.7.1 Mining Methodology and User Interaction Issues -- 1.7.2 Performance Issues -- 1.7.3 Issues Relating to the Diversity of Database Types -- 1.8 Data Mining Strategies -- 1.8.1 Classification -- 1.8.2 Association -- 1.8.3 Clustering -- 1.8.3.1 k-Means algorithm -- 1.8.4 Estimation -- 1.9 Data Mining: Ever Increasing Range of Applications -- 1.9.1 Games -- 1.9.2 Business -- 1.9.3 Science and Engineering -- 1.9.4 Human Rights -- 1.9.5 Medical Data Mining -- 1.9.6 Spatial Data Mining -- 1.9.7 Challenges in Spatial Mining -- 1.9.8 Temporal Data Mining -- 1.9.9 Sensor Data Mining -- 1.9.10 Visual Data Mining -- 1.9.11 Music Data Mining -- 1.9.12 Pattern Mining -- 1.9.13 Subject-based Data Mining -- 1.9.14 Knowledge Grid -- 1.10 Trends in Data Mining -- 1.10.1 Application Exploration -- 1.10.2 Scalable and Interactive Data Mining Methods.

1.10.3 Integration of Data Mining with Database Systems, Data Warehouse Systems, and Web Database Systems -- 1.10.4 Standardization of Data Mining Query Language -- 1.10.5 Visual Data Mining -- 1.10.6 New Methods for Mining Complex Types of Data -- 1.10.7 Biological Data Mining -- 1.10.8 Data Mining and Software Engineering -- 1.10.9 Web Mining -- 1.10.10 Distributed Data Mining -- 1.10.11 Real-Time Data Mining -- 1.10.12 Multi-Database Data Mining -- 1.10.13 Privacy Protection and Information Security in Data Mining -- 1.11 Classification Techniques in Data Mining -- 1.11.1 Definition of the Classification -- 1.11.2 Issues Regarding Classification -- 1.11.3 Evaluation Methods for Classification -- 1.11.4 Classifications Techniques -- 1.11.4.1 Tree structure -- 1.11.4.2 Rule-based algorithm -- 1.11.4.3 Distance-based algorithms -- 1.11.4.4 Neural networks-based algorithms -- 1.11.4.5 Statistical-based algorithms -- 1.12 Applications of Classifications -- 1.12.1 Target Marketing -- 1.12.2 Disease Diagnosis -- 1.12.3 Supervised Event Detection -- 1.12.4 Multimedia Data Analysis -- 1.12.5 Biological Data Analysis -- 1.12.6 Document Categorization and Filtering -- 1.12.7 Social Network Analysis -- 1.13 WEKA: An Effective Tool for Data Mining -- 1.13.1 Main Features of theWeka -- 1.13.2 Weka Interface -- 1.13.3 Weka for Classification -- 1.13.3.1 Selecting a classifier -- 1.13.3.2 Test options -- 1.14 WhatWe Aim to Cover Through the Present Book -- Chapter 2 - Current Literature Assessment in Data and Web Mining -- 2.1 Big Data and Its Mining -- 2.2 Data-Processing Basics -- 2.3 Data Mining -- 2.4 PioneeringWork -- 2.5 Algorithms Used in Data Mining -- 2.6 Classification and Mining -- 2.7 Performance Metrics of Classification/Mining -- 2.8 Data Mining forWeb -- 2.9 Categories ofWeb Data Mining -- 2.10 Radial Basis Function Networks.

2.11 J48 Decision Tree -- 2.12 Naive Bayes -- 2.13 Support Vector Machine (SVM) -- 2.14 Conclusion andWay Forward -- Chapter 3 - DataSet Creation for Web Mining -- 3.1 Introduction -- 3.2 Web Mining-Emerging Model of Business -- 3.2.1 Introduction toWeb Mining -- 3.3 Tools Used for Acquisition of Parameters -- 3.3.1 Accessibility -- 3.3.2 Design -- 3.3.3 Texts -- 3.3.4 Multimedia -- 3.3.5 Networking -- 3.4 Difficulties Encountered -- 3.4.1 Internet Problem -- 3.4.2 Preparation and Selection ofWebsites -- 3.4.3 Difficulty in Selecting Analysis Tool -- 3.4.4 Unavailability of Data -- 3.5 Flowchart -- 3.6 Freezing Parameters -- 3.6.1 Data Preprocessing -- 3.6.1.1 Data Preprocessing Techniques -- 3.6.2 Preprocessing and Filtering -- 3.6.2.1 Preprocessed and Filtered Overall Data -- 3.6.2.2 Preprocessed and FilteredWeb Accessibility Data -- 3.6.2.3 Preprocessed and Filtered Design Data -- 3.6.2.4 Preprocessed and Filtered Texts Data -- 3.6.2.5 Preprocessed and Filtered Multimedia Data -- 3.6.2.6 Preprocessed and Filtered Networking Dat -- 3.7 Way Forward -- Chapter 4 - Classification of Websites -- 4.1 Introduction -- 4.1.1 Accessibility -- 4.1.2 Design -- 4.1.3 Texts -- 4.1.4 Multimedia -- 4.1.5 Networking -- 4.2 Classification ofWebsites on Accessibility -- 4.2.1 Dataset -- 4.2.2 Clustering -- 4.2.3 Clustered Instances -- 4.2.4 Classification Via Clustering -- 4.2.4.1 Classification via clustering using J48 algorithm -- 4.2.4.2 Classification via clustering using RBFNetwork algorithm -- 4.2.4.3 Classification via clustering using NaiveBayes algorithm -- 4.2.4.4 Classification via clustering using SMO algorithm -- 4.2.4.5 Comparison of above classification algorithms -- 4.3 Classification Based onWebsite Design -- 4.3.1 Attribute Selection -- 4.3.2 Clustering -- 4.3.3 Cluster Analysis -- 4.3.4 Classification Through Clustering.

4.3.4.1 Classification via clustering using J48 algorithm -- 4.3.4.2 Classification via clustering using RBFNetwork algorithm -- 4.3.4.3 Classification via clustering using NaiveBayes algorithm -- 4.3.4.4 Classification via clustering using SMO algorithm -- 4.3.4.5 Comparison of above classification algorithms -- 4.4 Classification Based on Text -- 4.4.1 Feature Selection -- 4.4.2 Clustering -- 4.4.3 Cluster Analysis -- 4.4.4 Classification Through Clustering -- 4.4.4.1 Classification via clustering using J48 algorithm -- 4.4.4.2 Classification via clustering using RBFNetwork algorithm -- 4.4.4.3 Classification via clustering using NaiveBayes algorithm -- 4.4.4.4 Classification via clustering using SMO algorithm -- 4.4.4.5 Comparison of above classification algorithms -- 4.5 Classification Based on Multimedia Content of Websites -- 4.5.1 Feature Selection -- 4.5.2 Clustering -- 4.5.3 Cluster Analysis -- 4.5.4 Classification Through Clustering -- 4.5.4.1 Classification via clustering using J48 algorithm -- 4.5.4.2 Classification via clustering using RBFNetwork algorithm -- 4.5.4.3 Classification via clustering using NaiveBayes algorithm -- 4.5.4.4 Classification via clustering using SMO algorithm -- 4.5.4.5 Comparison of above classification algorithm -- 4.6 Classification Based on Network Analysis ofWebpage -- 4.6.1 Feature Selection -- 4.6.2 Clustering -- 4.6.3 Observations -- 4.6.4 Classification Through Clustering -- 4.6.4.1 Classification via clustering using J48 algorithm -- 4.6.4.2 Classification via clustering using RBFNetwork algorithm -- 4.6.4.3 Classification via clustering using NaiveBayes algorithm -- 4.6.4.4 Classification via clustering using SMO algorithm -- 4.6.4.5 Comparison of the above classification algorithm -- 4.7 Classification ofWebsites Using Overall Performance -- 4.7.1 Clustering -- 4.7.2 Cluster Analysis.

4.7.3 Classification Via Clustering -- 4.7.3.1 Classification via clustering using J48 algorithm -- 4.7.3.2 Classification via clustering using RBFNetwork algorithm -- 4.7.3.3 Classification via clustering using NaiveBayes algorithm -- 4.7.3.4 Classification via clustering using SMO algorithm -- 4.7.3.5 Comparison of the above classification algorithms -- 4.8 Results at a Glance and Conclusion -- 4.9 Summary and Future Directions -- Index -- About the Authors -- Back Cover.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.