Text Mining : Concepts, Implementation, and Big Data Challenge.

Format:

Book

Author/Creator:

Jo, Taeho.

Series:

Studies in Big Data Series

Studies in Big Data Series ; v.45

Language:

English

Physical Description:

1 online resource (451 pages)

Edition:

2nd ed.

Place of Publication:

Cham : Springer, 2025.

Summary:

This popular book, updated as a textbook for classroom use, discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management. The book features exercises and code to help readers quickly learn and apply knowledge.

Contents:

Intro

Preface

Contents

Part I Foundation

1 Introduction

1.1 Definition of Text Mining

1.2 Texts

1.2.1 Text Components

1.2.2 Text Formats

1.3 Data Mining Tasks

1.3.1 Classification

1.3.2 Clustering

1.3.3 Association

1.4 Data Mining Types

1.4.1 Relational Data Mining

1.4.2 Web Mining

1.4.3 Big Data Mining

1.5 Summary

References

2 Text Indexing

2.1 Overview of Text Indexing

2.2 Steps of Text Indexing

2.2.1 Tokenization

2.2.2 Stemming

2.2.3 Stop-Word Removal

2.2.4 Term Weighting

2.3 Text Indexing: Implementation

2.3.1 Class Definition

2.3.2 Stemming Rule

2.3.3 Method Implementations

2.4 Additional Steps

2.4.1 Index Filtering

2.4.2 Index Expansion

2.4.3 Index Optimization

2.5 Summary

3 Text Encoding

3.1 Overview of Text Encoding

3.2 Feature Selection

3.2.1 Wrapper Approach

3.2.2 Principal Component Analysis

3.2.3 Independent Component Analysis

3.2.4 Singular Value Decomposition

3.3 Feature Value Assignment

3.3.1 Assignment Schemes

3.3.2 Similarity Computation

3.4 Issues of Text Encoding

3.4.1 Huge Dimensionality

3.4.2 Sparse Distribution

3.4.3 Poor Transparency

3.5 Summary

4 Text Association

4.1 Overview of Text Association

4.2 Data Association

4.2.1 Functional View

4.2.2 Support and Confidence

4.2.3 Apriori Algorithm

4.3 Word Association

4.3.1 Word Text Matrix

4.3.2 Functional View

4.3.3 Simple Example

4.4 Text Association

4.4.1 Functional View

4.4.2 Simple Example

4.5 Overall Summary

Part II Text Categorization

5 Text Categorization: Conceptual View

5.1 Definition of Text Categorization

5.2 Data Classification

5.2.1 Binary Classification

5.2.2 Multiple Classification

5.2.3 Classification Decomposition.

5.2.4 Regression

5.3 Classification Types

5.3.1 Hard vs. Soft Classification

5.3.2 Flat vs. Hierarchical Classification

5.3.3 Single vs. Multiple Viewed Classification

5.3.4 Independent vs. Dependent Classification

5.4 Variants of Text Categorization

5.4.1 Spam Mail Filtering

5.4.2 Sentimental Analysis

5.4.3 Information Filtering

5.4.4 Topic Routing

5.5 Summary and Further Discussions

6 Text Categorization: Approaches

6.1 Machine Learning

6.2 Lazy Learning

6.2.1 K-Nearest Neighbor

6.2.2 Radius Nearest Neighbor

6.2.3 Distance-Based Nearest Neighbor

6.2.4 Attribute Discriminated Nearest Neighbor

6.3 Probabilistic Learning

6.3.1 Bayes Rule

6.3.2 Bayes Classifier

6.3.3 Naive Bayes

6.3.4 Bayesian Learning

6.4 Kernel-Based Classifier

6.4.1 Perceptron

6.4.2 Kernel Functions

6.4.3 Support Vector Machine

6.4.4 Optimization Constraints

6.5 Summary and Further Discussions

7 Text Categorization: Implementation

7.1 System Architecture

7.2 Class Definitions

7.2.1 Classes: Word, Text, and PlainText

7.2.2 Interface and Class: Classifier and KNearestNeighbor

7.2.3 Class: TextClassificationAPI

7.3 SubsectionTitle

7.3.1 Class: Word

7.3.2 Class: PlainText

7.3.3 Class: KNearestNeighbor

7.3.4 Class: TextClassificationAPI

7.4 Graphic User Interface and Demonstration

7.4.1 Class: TextClassificationGUI

7.4.2 Preliminary Tasks and Encoding

7.4.3 Classification Process

7.4.4 System Upgrading

7.5 Summary and Further Discussions

8 Text Categorization: Evaluation

8.1 Evaluation Overview

8.2 Text Collections

8.2.1 NewsPage.com

8.2.2 20NewsGroups

8.2.3 Reuter21578

8.2.4 OSHUMED

8.3 F1 Measure

8.3.1 Contingency Table

8.3.2 Micro-Averaged F1

8.3.3 Macro-Averaged F1

8.3.4 Example.

8.4 Statistical t-Test

8.4.1 Student t-Distribution

8.4.2 Unpaired Difference Inference

8.4.3 Paired Difference Inference

8.4.4 Example

8.5 Summary and Further Discussions

Part III Text Clustering

9 Text Clustering: Conceptual View

9.1 Definition of Text Clustering

9.2 Data Clustering

9.2.1 SubSubsectionTitle

9.2.2 Association vs. Clustering

9.2.3 Classification vs. Clustering

9.2.4 Constraint Clustering

9.3 Clustering Types

9.3.1 Static vs. Dynamic Clustering

9.3.2 Crisp vs. Fuzzy Clustering

9.3.3 SubsectionTitle

9.3.4 Single vs. Multiple Viewed Clustering

9.4 Derived Tasks from Text Clustering

9.4.1 Cluster Naming

9.4.2 Subtext Clustering

9.4.3 Automatic Sampling for Text Categorization

9.4.4 Redundant Project Detection

9.5 Summary and Further Discussions

10 Text Clustering: Approaches

10.1 Unsupervised Learning

10.2 Simple Clustering Algorithms

10.2.1 AHC Algorithm

10.2.2 Divisive Clustering Algorithm

10.2.3 Single-Pass Algorithm

10.2.4 Growing Algorithm

10.3 K-Means Algorithm

10.3.1 Crisp K-Means Algorithm

10.3.2 Fuzzy K-Means Algorithm

10.3.3 Gaussian Mixture

10.3.4 K Medoid Algorithm

10.4 Competitive Learning

10.4.1 Kohonen Networks

10.4.2 Learning Vector Quantization

10.4.3 Two-Dimensional Self-Organizing Map

10.4.4 Neural Gas

10.5 Summary and Further Discussions

11 Text Clustering: Implementation

11.1 System Architecture

11.2 Class Definitions

11.2.1 Classes in Text Categorization System

11.2.2 Class: Cluster

11.2.3 Interface: ClusterAnalyzer

11.2.4 Class: AHCAlgorithm

11.3 Method Implementations

11.3.1 Methods in Previous Classes

11.3.2 Class: Cluster

11.3.3 Class: AHC Algorithm

11.4 Class: ClusterAnalysisAPI.

11.4.1 Class: ClusterAnalysisAPI

11.4.2 Class: ClusterAnalyzerGUI

11.4.3 Demonstration

11.4.4 System Upgrading

11.5 Summary and Further Discussions

Reference

12 Text Clustering: Evaluation

12.1 Introduction

12.2 Cluster Validations

12.2.1 Intra-cluster and Inter-cluster Similarities

12.2.2 Internal Validation

12.2.3 Relative Validation

12.2.4 External Validation

12.3 Clustering Index

12.3.1 Computation Process

12.3.2 Evaluation of Crisp Clustering

12.3.3 Evaluation of Fuzzy Clustering

12.3.4 Evaluation of Hierarchical Clustering

12.4 Parameter Tuning

12.4.1 Clustering Index for Unlabeled Documents

12.4.2 Simple Clustering Algorithm with Parameter Tuning

12.4.3 K Means Algorithm with Parameter Tuning

12.4.4 Evolutionary Clustering Algorithm

12.5 Summary and Further Discussions

Part IV Advanced Topics

13 Text Summarization

13.1 Definition of Text Summarization

13.2 Text Summarization Types

13.2.1 Manual Versus Automatic Text Summarization

13.2.2 Single Versus Multiple Text Summarization

13.2.3 Flat Versus Hierarchical Text Summarization

13.2.4 Abstraction Versus Query-Based Summarization

13.3 Approaches to Text Summarization

13.3.1 Heuristic Approaches

13.3.2 Mapping into Classification Task

13.3.3 Sampling Schemes

13.3.4 Application of Machine Learning Algorithms

13.4 Combination with Other Text Mining Tasks

13.4.1 Summary-Based Classification

13.4.2 Summary-Based Clustering

13.4.3 Topic-Based Summarization

13.4.4 Text Expansion

13.5 Summary and Further Discussions

14 Text Segmentation

14.1 Definition of Text Segmentation

14.2 Text Segmentation Type

14.2.1 Spoken Versus Written Text Segmentation

14.2.2 Ordered Versus Unordered Text Segmentation

14.2.3 Exclusive Versus Overlapping Segmentation.

14.2.4 Flat Versus Hierarchical Text Segmentation

14.3 Machine Learning-Based Approaches

14.3.1 Heuristic Approaches

14.3.2 Mapping into Classification

14.3.3 Encoding Adjacent Paragraph Pairs

14.3.4 Application of Machine Learning

14.4 Derived Tasks

14.4.1 Temporal Topic Analysis

14.4.2 Subtext Retrieval

14.4.3 Subtext Synthesization

14.4.4 Virtual Text

14.5 Summary and Further Discussions

15 Taxonomy Generation

15.1 Definition of Taxonomy Generation

15.2 Relevant Tasks to Taxonomy Generation

15.2.1 Keyword Extraction

15.2.2 Word Categorization

15.2.3 Word Clustering

15.2.4 Topic Routing

15.3 Taxonomy Generation Schemes

15.3.1 Index-Based Scheme

15.3.2 Clustering-Based Scheme

15.3.3 Association-Based Scheme

15.3.4 Link Analysis-Based Scheme

15.4 Taxonomy Governance

15.4.1 Taxonomy Maintenance

15.4.2 Taxonomy Growth

15.4.3 Taxonomy Integration

15.4.4 Ontology

15.5 Summary and Further Discussions

16 Dynamic Document Organization

16.1 Definition of Dynamic Document Organization

16.2 Online Clustering

16.2.1 Online Clustering in Functional View

16.2.2 Online K Means Algorithm

16.2.3 Online Unsupervised KNN Algorithm

16.2.4 Online Fuzzy Clustering

16.3 Dynamic Organization

16.3.1 Execution Process

16.3.2 Maintenance Mode

16.3.3 Creation Mode

16.3.4 Additional Tasks

16.4 Issues of Dynamic Document Organization

16.4.1 Text Representation

16.4.2 Binary Decomposition

16.4.3 Transition into Creation Mode

16.4.4 Variants of DDO System

16.4.5 Summary and Further Discussions

Part V Word Mining

17 Word Encoding

17.1 Introduction

17.2 Word Encoding

17.2.1 Text Indexing

17.2.2 Text Index Structure

17.2.3 Word Indexing

17.2.4 Inverted Index

17.3 Word Representation.

17.3.1 Text Representation.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9783031759765

3031759761

OCLC:

1481791252