書籍詳細

書籍詳細




洋書

教師なしアンサンブル学習を通じた時間データマイニング

Temporal Data Mining Via Unsupervised Ensemble Learning

Yang, Yun

Elsevier Science Ltd 2016/11
172 p. 23 cm   
装丁: Pap   
版表示など: pap.    装丁について
テキストの言語: ENG    出版国: US
ISBN: 9780128116548
KCN: 1025980484
紀伊國屋書店 選定タイトル
標準価格:¥10,310(本体 ¥9,373)   
Web販売価格あり    Web販売価格について

為替レートの変動や出版社の都合によって、価格が変動する場合がございます。

この商品は提携先の海外出版社在庫からの取り寄せとなります。品切れの場合、恐れ入りますがご了承下さい。

納期について
DDC: 005
KDC: F88 データベース
関連書リスト: SB2849 データサイエンス 2017
ご購入を希望される方は、
下のリンクをクリックしてください。

Annotation

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.

Full Description

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.

Table of Contents

1. Introduction 2. Temporal Data Mining 3. Temporal Data Clustering 4. Ensemble Learning 5. HMM-Based Hybrid Meta-Clustering in Association With Ensemble Technique 6. Unsupervised Learning via an Iteratively Constructed Clustering Ensemble 7. Temporal Data Clustering via a Weighted Clustering Ensemble With Different Representations 8. Conclusions, Future Work