Cargando…
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Elsevier Science,
2016.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000Ma 4500 | ||
|---|---|---|---|
| 001 | SCIDIR_ocn963702404 | ||
| 003 | OCoLC | ||
| 005 | 20231120112155.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 161118s2016 xx ob 001 0 eng d | ||
| 040 | |a IDEBK |b eng |e pn |c IDEBK |d N$T |d YDX |d EBLCP |d NLE |d OCLCO |d OPELS |d OCLCQ |d IDEBK |d OCLCF |d N$T |d UMI |d STF |d IDB |d UPM |d OCLCQ |d OTZ |d OCLCQ |d U3W |d MERUC |d REB |d D6H |d CEF |d KSU |d DEBBG |d AU@ |d OCLCQ |d LVT |d CNCGM |d LQU |d CNCEN |d MUU |d UKMGB |d OCLCQ |d VT2 |d OCLCQ |d S2H |d OCLCO |d OCLCQ |d OCLCO |d OCLCQ |d OCLCO | ||
| 015 | |a GBB6J1947 |2 bnb | ||
| 016 | 7 | |a 018115335 |2 Uk | |
| 019 | |a 964298618 |a 964545062 |a 967513911 |a 1105190131 |a 1105564692 |a 1135216337 |a 1192336670 |a 1235825098 |a 1240512611 |a 1262690350 | ||
| 020 | |a 0128118415 |q (electronic bk.) | ||
| 020 | |a 9780128118412 |q (electronic bk.) | ||
| 020 | |z 0128116544 | ||
| 020 | |z 9780128116548 | ||
| 035 | |a (OCoLC)963702404 |z (OCoLC)964298618 |z (OCoLC)964545062 |z (OCoLC)967513911 |z (OCoLC)1105190131 |z (OCoLC)1105564692 |z (OCoLC)1135216337 |z (OCoLC)1192336670 |z (OCoLC)1235825098 |z (OCoLC)1240512611 |z (OCoLC)1262690350 | ||
| 050 | 4 | |a QA76.9.D343 | |
| 072 | 7 | |a COM |x 000000 |2 bisacsh | |
| 082 | 0 | 4 | |a 006.312 |2 23 |
| 100 | 1 | |a Yang, Yun |c (University lecturer) | |
| 245 | 1 | 0 | |a Temporal Data Mining via Unsupervised Ensemble Learning. |
| 260 | |b Elsevier Science, |c 2016. | ||
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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 D 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Temporal databases. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 2 | |a Data Mining |0 (DNLM)D057225 | |
| 650 | 2 | |a Machine Learning |0 (DNLM)D000069550 | |
| 650 | 6 | |a Exploration de donn�ees (Informatique) |0 (CaQQLa)201-0300292 | |
| 650 | 6 | |a Bases de donn�ees spatio-temporelles. |0 (CaQQLa)201-0244241 | |
| 650 | 6 | |a Apprentissage automatique. |0 (CaQQLa)201-0131435 | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Data mining |2 fast |0 (OCoLC)fst00887946 | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Temporal databases |2 fast |0 (OCoLC)fst01147471 | |
| 776 | 0 | 8 | |i Print version: |a Yang, Yun. |t Temporal Data Mining via Unsupervised Ensemble Learning. |d Elsevier Science, 2016 |z 0128116544 |z 9780128116548 |w (OCoLC)954534995 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128116548 |z Texto completo |