Web mining : a synergic approach resorting to classifications and clustering / V. S. Kumbhar, K.S. Oza and R. K. Kamat.

Format:

Book

Author/Creator:

Kumbhar, V. S., author.

Kamat, R. K., author.

Oza, K. S., author.

Series:

River Publishers series in information science and technology.

River Publishers Series in Information Science and Technology

Language:

English

Subjects (All):

Web databases.

Data mining.

Cluster analysis.

Physical Description:

1 online resource (232 pages) : illustrations (some color), tables, graphs.

Edition:

1st ed.

Place of Publication:

Gistrup, Denmark : River Publishers, [2016]

Summary:

Web Mining: A Synergic Approach Resorting to Classifications and Clustering showcases an effective methodology for classification and clustering of web sites from their usability point of view.

Contents:

Cover

Half Title

Series Page

Title Page

Copyright Page

Table of Contents

Preface

Acknowledgment

List of Figures

List of Tables

List of Graphs

List of Abbreviations

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 the Weka

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 What We Aim to Cover Through the Present Book

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 Pioneering Work

2.5 Algorithms Used in Data Mining

2.6 Classification and Mining

2.7 Performance Metrics of Classification/Mining

2.8 Data Mining for Web

2.9 Categories of Web 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 and Way Forward

3: DataSet Creation for Web Mining

3.1 Introduction

3.2 Web Mining-Emerging Model of Business.

3.2.1 Introduction to Web 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 of Websites

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 Filtered Web 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 Data

3.7 Way Forward

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 of Websites 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 on Website 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 of Webpage

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 of Websites 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.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

1-000-79267-6

1-00-334003-2

1-003-34003-2

1-000-79536-5

87-93379-84-6

9781003340034

OCLC:

967546880