| Description |
1 online resource (viii, 291 pages) : illustrations (some color) |
| Series |
Studies in Big Data, 2197-6503 ; volume 3 |
|
Studies in big data ; v. 3. 2197-6503
|
| Contents |
Part I. Fundamental Technologies Supporting Service Science -- Data Mining for Service / Katsutoshi Yada -- Feature Selection Over Distributed Data Streams / Jacob Kogan -- Learning Hidden Markov Models Using Probabilistic Matrix Factorization / Ashutosh Tewari and Michael J. Giering -- Dimensionality Reduction for Information Retrieval Using Vector Replacement of Rare Terms / Tobias Berka and Marian Vajteršic -- Panel Data Analysis Via Variable Selection and Subject Clustering / Haibing Lu, Shengsheng Huang, Yingjiu Li and Yanjiang Yang -- Part II. Knowledge Discovery from Text -- A Weighted Density-Based Approach for Identifying Standardized Items that are Significantly Related to the Biological Literature / Omar Al-Azzam, Jianfei Wu, Loai Al-Nimer, Charith Chitraranjan and Anne M. Denton -- Nonnegative Tensor Factorization of Biomedical Literature for Analysis of Genomic Data / Sujoy Roy, Ramin Homayouni, Michael W. Berry and Andrey A. Puretskiy -- Text Mining of Business-Oriented Conversations at a Call Center / Hironori Takeuchi and Takahira Yamaguchi -- Part III. Approach for New Services in Social Media -- Scam Detection in Twitter / Xiaoling Chen, Rajarathnam Chandramouli and Koduvayur P. Subbalakshmi -- A Matrix Factorization Framework for Jointly Analyzing Multiple Nonnegative Data Sources / Sunil Kumar Gupta, Dinh Phung, Brett Adams and Svetha Venkatesh -- Recommendation Systems for Web 2.0 Marketing / Chen Wei, Richard Khoury and Simon Fong -- Part IV. Data Mining Spreading into Various Service Fields -- Handling Imbalanced and Overlapping Classes in Smart Environments Prompting Dataset / Barnan Das, Narayanan C. Krishnan and Diane J. Cook -- Change Detection from Heterogeneous Data Sources / Tsuyoshi Idé -- Interesting Subset Discovery and Its Application on Service Processes / Maitreya Natu and Girish Keshav Palshikar -- Text Document Cluster Analysis Through Visualization of 3D Projections / Masaki Aono and Mei Kobayashi |
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Part I. Fundamental Technologies Supporting Service Science -- Part II. Knowledge Discovery from Text -- Part III. Approach for New Services in Social Media -- Part IV. Data Mining Spreading into Various Service Fields |
| Summary |
Virtually all nontrivial and modern service related problems and systems involve data volumes and types that clearly fall into what is presently meant as "big data", that is, are huge, heterogeneous, complex, distributed, etc. Data mining is a series of processes which include collecting and accumulating data, modeling phenomena, and discovering new information, and it is one of the most important steps to scientific analysis of the processes of services. Data mining application in services requires a thorough understanding of the characteristics of each service and knowledge of the compatibility of data mining technology within each particular service, rather than knowledge only in calculation speed and prediction accuracy. Varied examples of services provided in this book will help readers understand the relation between services and data mining technology. This book is intended to stimulate interest among researchers and practitioners in the relation between data mining technology and its application to other fields |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed January 6, 2014) |
|
Print version record |
| In |
Springer eBooks |
| Subject |
Data mining.
|
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Big data.
|
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Data Mining
|
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COMPUTERS -- General.
|
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Ingénierie.
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Big data
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Data mining
|
| Form |
Electronic book
|
| Author |
Yada, Katsutoshi, editor
|
| ISBN |
9783642452529 |
|
3642452523 |
|
3642452515 |
|
9783642452512 |
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