Temporal data mining via unsupervised ensemble learning / Yun Yang.

Format:

Book

Author/Creator:

Yang, Yun, author.

Language:

English

Subjects (All):

Data mining.

Temporal databases.

Physical Description:

1 online resource (174 pages) : color illustrations

Edition:

First edition.

Place of Publication:

Waltham, MA : Elsevier, [2017]

System Details:

text file

Summary:

Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics. Includes fundamental concepts and knowledge, covering all key tasks and techniques of temporal data mining, i.e., temporal data representations, similarity measure, and mining tasks Concentrates on temporal data clustering tasks from different perspectives, including major algorithms from clustering algorithms and ensemble learning approaches Presents a rich blend of theory and practice, addressing seminal research ideas and looking at the technology from a practical point-of-view

Contents:

Front Cover

Temporal Data Mining via Unsupervised Ensemble Learning

Copyright

Contents

List of Figures

List of Tables

Acknowledgments

1 - Introduction

1.1 BACKGROUND

1.2 PROBLEM STATEMENT

1.3 OBJECTIVE OF BOOK

1.4 OVERVIEW OF BOOK

2 - Temporal Data Mining

2.1 INTRODUCTION

2.2 REPRESENTATIONS OF TEMPORAL DATA

2.2.1 TIME DOMAIN-BASED REPRESENTATIONS

2.2.2 TRANSFORMATION-BASED REPRESENTATIONS

Piecewise Local Statistics

Piecewise Discrete Wavelet Transforms

Polynomial Curve Fitting

Discrete Fourier Transforms

2.2.3 GENERATIVE MODEL-BASED REPRESENTATIONS

2.3 SIMILARITY MEASURES

2.3.1 SIMILARITY IN TIME

2.3.2 SIMILARITY IN SHAPE

2.3.3 SIMILARITY IN CHANGE

2.4 MINING TASKS

2.5 SUMMARY

3 - Temporal Data Clustering

3.1 INTRODUCTION

3.2 OVERVIEW OF CLUSTERING ALGORITHMS

3.2.1 PARTITIONAL CLUSTERING

K-means

Hidden Markov Model-Based K-Models Clustering

3.2.2 HIERARCHICAL CLUSTERING

Single Linkage

Complete Linkage

Average Linkage

HMM-Based Agglomerative Clustering

HMM-Based Divisive Clustering

3.2.3 DENSITY-BASED CLUSTERING

Density-Based Spatial Clustering of Applications with Noise

3.2.4 MODEL-BASED CLUSTERING

EM Algorithm

HMM-Based Hybrid Partitional-Hierarchical Clustering

HMM-Based Hierarchical Metaclustering

3.3 CLUSTERING VALIDATION

3.3.1 CLASSIFICATION ACCURACY

3.3.2 ADJUSTED RAND INDEX

3.3.3 JACCARD INDEX

3.3.4 MODIFIED HUBERT'S Γ INDEX

3.3.5 DUNN'S VALIDITY INDEX

3.3.6 DAVIES-BOULDIN VALIDITY INDEX

3.3.7 NORMALIZED MUTUAL INFORMATION

3.4 SUMMARY

4 - Ensemble Learning

4.1 INTRODUCTION

4.2 ENSEMBLE LEARNING ALGORITHMS

Bagging

Boosting

4.3 COMBINING METHODS

Linear Combiner

Product Combiner.

Majority Voting Combiner

4.4 DIVERSITY OF ENSEMBLE LEARNING

4.5 CLUSTERING ENSEMBLE

4.5.1 CONSENSUS FUNCTIONS

4.5.1.1 Hypergraphic Partitioning Approach

Cluster-Based Similarity Partitioning Algorithm

Hypergraph-Partitioning Algorithm

Meta-Clustering Algorithm

4.5.1.2 Coassociation-Based Approach

4.5.1.3 Voting-Based Approach

4.5.2 OBJECTIVE FUNCTION

4.6 SUMMARY

5 - HMM-Based Hybrid Meta-Clustering in Association With Ensemble Technique

5.1 INTRODUCTION

5.2 HMM-BASED HYBRID META-CLUSTERING ENSEMBLE

5.2.1 MOTIVATION

5.2.2 MODEL DESCRIPTION

5.3 SIMULATION

5.3.1 HMM-GENERATED DATA SET

5.3.2 CBF DATA SET

5.3.3 TIME SERIES BENCHMARKS

5.3.4 MOTION TRAJECTORY

5.4 SUMMARY

6 - Unsupervised Learning via an Iteratively Constructed Clustering Ensemble

6.1 INTRODUCTION

6.2 ITERATIVELY CONSTRUCTED CLUSTERING ENSEMBLE

6.2.1 MOTIVATION

6.2.2 MODEL DESCRIPTION

6.3 SIMULATION

6.3.1 CYLINDER-BELL-FUNNEL DATA SET

6.3.2 TIME SERIES BENCHMARKS

6.3.3 MOTION TRAJECTORY

6.4 SUMMARY

7 - Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations

7.1 INTRODUCTION

7.2 WEIGHTED CLUSTERING ENSEMBLE WITH DIFFERENT REPRESENTATIONS OF TEMPORAL DATA

7.2.1 MOTIVATION

7.2.2 MODEL DESCRIPTION

7.2.3 WEIGHTED CONSENSUS FUNCTION

Partition Weighting Scheme

Weighted Similarity Matrix

Candidate Consensus Partition Generation

7.2.4 AGREEMENT FUNCTION

7.2.5 ALGORITHM ANALYSIS

7.3 SIMULATION

7.3.1 TIME SERIES BENCHMARKS

7.3.2 MOTION TRAJECTORY

7.3.3 TIME-SERIES DATA STREAM

7.4 SUMMARY

8 - Conclusions, Future Work

Appendix

A.1 WEIGHTED CLUSTERING ENSEMBLE ALGORITHM ANALYSIS

A.2 IMPLEMENTATION OF HMM-BASED META-CLUSTERING ENSEMBLE IN MATLAB CODE.

A.3 IMPLEMENTATION OF ITERATIVELY CONSTRUCTED CLUSTERING ENSEMBLE IN MATLAB CODE

A.4 IMPLEMENTATION OF WCE WITH DIFFERENT REPRESENTATIONS

References

Index

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

R

S

T

V

W

Back Cover.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9780128118412

0128118415

OCLC:

967513911