Temporal data mining via unsupervised ensemble learning

Temporal data mining via unsupervised ensemble learning

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...

Descripción completa

Detalles Bibliográficos
Otros Autores: Yang, Yun, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Waltham, MA : Elsevier [2017]
Edición:First edition
Materias:
Data mining.
Temporal databases.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630455006719
Tabla de Contenidos:
  • Front Cover
  • Temporal Data Mining via Unsupervised Ensemble Learning
  • 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.

Ejemplares similares