Mining multimedia documents /
| Corporate Author: | |
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| Other Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton :
CRC Press,
[2017]
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Halftitle Page; Title Page; Copyright Page; Contents; Preface; Objective of the Book; Target Audience; Organization of the Book; Conclusion; Editors; Contributors; Section I Motivation and Problem Definition; 1. Mining Multimedia Documents: An Overview; 1.1 Introduction; 1.2 Multimedia Mining Process; 1.3 Multimedia Data Mining Architecture; 1.4 Multimedia Data Mining Models; 1.4.1 Classification; 1.4.2 Clustering; 1.4.3 Association Rules; 1.4.4 Statistical Modeling; 1.5 Multimedia Mining: Image Mining; 1.5.1 Low-Level Image Processing; 1.5.2 High-Level Image Processing.
- 1.5.3 Application Using Image Data Mining1.5.4 Application of Image Data Mining in the Medical Field; 1.6 Text and Image Feature Retrieval: Data Fusion; 1.7 Audio Mining; 1.8 Video Mining; 1.9 Conclusion; References; Section II Text Mining Using NLP Techniques; 2. Fuzzy Logic for Text Document Clustering; 2.1 Introduction; 2.2 Background; 2.2.1 Fuzzy Logic; 2.2.1.1 Fuzzy Operators; 2.2.1.2 Membership Function; 2.2.1.3 Fuzzy Logic and Application Fields; 2.3 Proposed Approach for Document Clustering; 2.3.1 Collecting Documents; 2.3.2 Processing Documents; 2.3.2.1 Cleaning Documents.
- 2.3.2.2 Vector Representation of Documents2.3.3 Clustering Documents; 2.4 Experimentation and Test; 2.5 Conclusion; References; 3. Toward Modeling Semiautomatic Data Warehouses: Guided by Social Interactions; 3.1 Introduction; 3.2 State of the Art; 3.2.1 Approaches to Designing Data Warehouses; 3.2.1.1 Sources-Based Approaches; 3.2.1.2 Requirements-Based Approaches; 3.2.1.3 Mixed Approaches; 3.2.1.4 Comparative Study; 3.2.2 Social Network; 3.3 New Approach for Data Warehouse Design Based on Principal Component Analysis in Medical Social Network; 3.3.1 Functional Architecture; 3.3.2 Process.
- 3.3.2.1 Step 1: Specification of OLAP Requirement3.3.2.2 Step 2: Generation of Data Marts Schema; 3.3.2.3 Step 3: Generation of Data Warehouse Schema; 3.3.3 Algorithm; 3.3.4 Case Study; 3.3.4.1 Step 1: Specification OLAP Requirement; 3.3.4.2 Step 2: Generation of Data Marts Schema; 3.3.4.3 Step 3: Generation of Data Warehouse Schema; 3.4 Conclusion; References; 4. Multi-Agent System for Text Mining; 4.1 Introduction; 4.2 Natural Language Processing; 4.2.1 NLP Definition; 4.2.2 NLP Applications; 4.2.3 NLP Levels; 4.3 Text Mining; 4.3.1 A General Definition of Extracting Information from Texts.
- 4.3.2 Linguistic Approaches4.3.2.1 TERMINO; 4.3.2.2 LEXTER; 4.3.2.3 SYNTEX; 4.3.3 Statistical Approaches; 4.3.4 Hybrid Approaches; 4.3.4.1 ACABIT; 4.3.4.2 XTRACT; 4.3.4.3 TERMS; 4.4 Multi-Agent Systems; 4.4.1 Definition of a Multi-Agent System; 4.4.1.1 The Benefits and the Reasons for Using a Multi-Agent System; 4.4.2 Definitions of an Agent; 4.4.3 Types of Agents; 4.4.3.1 The Reactive Agents; 4.4.3.2 The Cognitive Agents; 4.4.3.3 Cognitive Agents versus Reactive Agents; 4.4.3.4 Hybrid Agent; 4.5 Multi-Agent System for Text Mining; 4.6 Conclusion and Perspective; References.