Mining multimedia documents

The information age has led to an explosion in the amount of information available to the individual and the means by which it is accessed, stored, viewed, and transferred. In particular, the growth of the internet has led to the creation of huge repositories of multimedia documents in a diverse range of scientific and professional fields, as well as the tools to extract useful knowledge from them. __Mining Multimedia Documents__ is a must-read for researchers, practitioners, and students working at the intersection of data mining and multimedia applications. It investigates various techniques related to mining multimedia documents based on text, image, and video features. It provides an insight into the open research problems benefitting advanced undergraduates, graduate students, researchers, scientists and practitioners in the fields of medicine, biology, production, education, government, national security and economics. 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 Mining -- 1.5.4 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 Documents -- 2.3.3 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 Content: Mining Multimedia Documents: An Overview. Fuzzy Decision Trees for Text Document Clustering. Towards Modeling Semi-Automatic Data Warehouses: Guided by Social Interactions. Multi-Agent System for Text Mining. The transformation of User Requirements in UML Diagrams: An Overview. An Overview of Information Extraction using Textual Case-Based Reasoning. Opinions Classification. Documents Classification Based on Text and Image Features. Content-Based Image Retrieval (CBIR). Mining Knowledge in Medical Image Databases. Segmentation for Medical Image Mining. Biological Data Mining: Techniques and Applications. Video Text Extraction and Mining. Recent Advancement in Multimedia Content using Deep Learning.