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035    (MiAaPQ)EBC4771354 
035    (Au-PeEL)EBL4771354 
035    (CaPaEBR)ebr11319675 
035    (OCoLC)967546880 
040    MiAaPQ|beng|erda|epn|cMiAaPQ|dMiAaPQ 
050  4 QA76.9.D343.K863 2016 
082 0  006.3/12 
100 1  Kumbhar, V. S 
245 10 Web Mining :|bA Synergic Approach Resorting to 
       Classifications and Clustering 
264  1 Aalborg :|bRiver Publishers,|c2017 
264  4 |c©2017 
300    1 online resource (232 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
490 1  River Publishers Series in Information Science and 
       Technology Ser 
505 0  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 
505 8  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 
505 8  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 
505 8  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 
505 8  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 
588    Description based on publisher supplied metadata and other
       sources 
590    Electronic reproduction. Ann Arbor, Michigan : ProQuest 
       Ebook Central, 2020. Available via World Wide Web. Access 
       may be limited to ProQuest Ebook Central affiliated 
       libraries 
650  0 Data mining 
655  4 Electronic books 
700 1  Oza, K. S 
700 1  Kamat, R. K 
776 08 |iPrint version:|aKumbhar, V. S.|tWeb Mining : A Synergic 
       Approach Resorting to Classifications and Clustering
       |dAalborg : River Publishers,c2017|z9788793379831 
830  0 River Publishers Series in Information Science and 
       Technology Ser 
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