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Data Mining for Service

  • Book
  • © 2014

Overview

Editors:
  1. Katsutoshi Yada

    1. Faculty of Commerce, Kansai University, Osaka, Japan

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  • Presents a new way for strategic
  • Use of Large-Scale Data Sets in Business
  • Demonstrates how Data Mining can be used to Revitalize your Business
  • Written by leading experts in the field
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Big Data (SBD, volume 3)

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-viii
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  2. Fundamental Technologies Supporting Service Science

    1. Front Matter

      Pages 1-1
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    2. Data Mining for Service

      • Katsutoshi Yada
      Pages 3-10

    3. Feature Selection Over Distributed Data Streams

      • Jacob Kogan
      Pages 11-26

    4. Learning Hidden Markov Models Using Probabilistic Matrix Factorization

      • Ashutosh Tewari, Michael J. Giering
      Pages 27-39

    5. Dimensionality Reduction for Information Retrieval Using Vector Replacement of Rare Terms

      • Tobias Berka, Marian Vajteršic
      Pages 41-60

    6. Panel Data Analysis Via Variable Selection and Subject Clustering

      • Haibing Lu, Shengsheng Huang, Yingjiu Li, Yanjiang Yang
      Pages 61-76

  3. Knowledge Discovery from Text

    1. Front Matter

      Pages 77-77
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    2. 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, Anne M. Denton
      Pages 79-96

    3. Nonnegative Tensor Factorization of Biomedical Literature for Analysis of Genomic Data

      • Sujoy Roy, Ramin Homayouni, Michael W. Berry, Andrey A. Puretskiy
      Pages 97-110

    4. Text Mining of Business-Oriented Conversations at a Call Center

      • Hironori Takeuchi, Takahira Yamaguchi
      Pages 111-129

  4. Approach for New Services in Social Media

    1. Front Matter

      Pages 131-131
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    2. Scam Detection in Twitter

      • Xiaoling Chen, Rajarathnam Chandramouli, Koduvayur P. Subbalakshmi
      Pages 133-150

    3. A Matrix Factorization Framework for Jointly Analyzing Multiple Nonnegative Data Sources

      • Sunil Kumar Gupta, Dinh Phung, Brett Adams, Svetha Venkatesh
      Pages 151-170

    4. Recommendation Systems for Web 2.0 Marketing

      • Chen Wei, Richard Khoury, Simon Fong
      Pages 171-196

  5. Data Mining Spreading into Various Service Fields

    1. Front Matter

      Pages 197-197
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    2. Handling Imbalanced and Overlapping Classes in Smart Environments Prompting Dataset

      • Barnan Das, Narayanan C. Krishnan, Diane J. Cook
      Pages 199-219

    3. Change Detection from Heterogeneous Data Sources

      • Tsuyoshi Idé
      Pages 221-243

    4. Interesting Subset Discovery and Its Application on Service Processes

      • Maitreya Natu, Girish Keshav Palshikar
      Pages 245-269

    5. Text Document Cluster Analysis Through Visualization of 3D Projections

      • Masaki Aono, Mei Kobayashi
      Pages 271-291
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Keywords

About this book

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.

Reviews

From the book reviews:

“This book is a must-read for data mining practitioners in the service sector, as it gives state-of-the-art information about new technologies, strategies, and devices that will help improve the efficiency and usefulness of data mining in this field. It will also be of interest to researchers, as data mining in the service sector is realizing its full potential and is an interesting field to be in since many cutting-edge technologies are at play here.” (Alexis Leon, Computing Reviews, June, 2014)

Editors and Affiliations

  • Faculty of Commerce, Kansai University, Osaka, Japan

    Katsutoshi Yada

Bibliographic Information

  • Book Title: Data Mining for Service

  • Editors: Katsutoshi Yada

  • Series Title: Studies in Big Data

  • DOI: https://doi.org/10.1007/978-3-642-45252-9

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2014

  • Hardcover ISBN: 978-3-642-45251-2Published: 16 January 2014

  • Softcover ISBN: 978-3-662-50743-8Published: 27 August 2016

  • eBook ISBN: 978-3-642-45252-9Published: 03 January 2014

  • Series ISSN: 2197-6503

  • Series E-ISSN: 2197-6511

  • Edition Number: 1

  • Number of Pages: VIII, 291

  • Number of Illustrations: 97 b/w illustrations, 12 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence, Operations Research/Decision Theory

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