Web Mining A Synergic Approach Resorting to Classifications and Clustering.

Web Mining A Synergic Approach Resorting to Classifications and Clustering.

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Bibliographic Details
Main Author: Kumbhar, V. S.
Other Authors: Oza, K. S., Kamat, R. K.
Format: Electronic eBook
Language:English
Published: Aalborg : River Publishers, 2017.
Series:River Publishers Series in Information Science and Technology Ser.
Subjects:
Web databases.
Data mining.
Cluster analysis.
Online Access: Full text (Emmanuel users only)
Table of Contents:
  • 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

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