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035 (MiAaPQ)EBC4771354
035 (Au-PeEL)EBL4771354
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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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