Data Mining for Service

291 Pages·2014·9.304 MB·English

by  Katsutoshi Yada (auth.), Katsutoshi Yada (eds.)

Studies in Big Data 3
Katsutoshi Yada E ditor
Data
Mining for
Service
Studies in Big Data
Volume 3
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
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About this Series
The series ‘‘Studies in Big Data’’ (SBD) publishes new developments and
advances in the various areas of Big Data- quickly and with a high quality. The
intentistocoverthetheory,research,development,andapplicationsofBigData,
as embedded in the fields of engineering, computer science, physics, economics
andlifesciences.Thebooksoftheseriesrefertotheanalysisandunderstandingof
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coming from sensors or other physical instruments as well as simulations, crowd
sourcing, social networks or other internet transactions, such as emails or video
click streams andother.Theseries containsmonographs,lecturenotesand edited
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artificial intelligence, data mining, modern statistics and Operations research, as
well as self-organizing systems. Of particular value to both the contributors and
thereadershiparetheshortpublicationtimeframeandtheworld-widedistribution,
which enable both wide and rapid dissemination of research output.
Katsutoshi Yada
Editor
Data Mining for Service
123
Editor
Katsutoshi Yada
Faculty ofCommerce
KansaiUniversity
Osaka
Japan
ISSN 2197-6503 ISSN 2197-6511 (electronic)
ISBN 978-3-642-45251-2 ISBN 978-3-642-45252-9 (eBook)
DOI 10.1007/978-3-642-45252-9
SpringerHeidelbergNewYorkDordrechtLondon
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(cid:2)Springer-VerlagBerlinHeidelberg2014
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Preface
In the globalized economy, the service sector is expanding rapidly and becoming
more and more important. Many researchers have conducted research on services
fromvariouspointsofviewandofferedinsightstobusinessowners.Recognizedas
one of the most important challenges in research on services among practitioners
and researchers is how to improve service productivity and efficiently ensure
customer satisfaction with limited natural and human resources. Due to their
nature,servicesusedtobedifficulttoresearchusingascientificapproach,butthe
innovation of digital devices has led to the accumulation of a variety of data,
which is gradually enabling researchers to analyze services scientifically.
Data mining is one of the most important steps to scientific analysis of service
processes. It is a series of processes which include collecting and accumulating
data,modelingphenomena,anddiscoveringnewinformation.Numeroustechnical
papersandstudiesondatamininghavebeenpublishedincomputerscience.Using
calculationspeedandpredictionaccuracyastheevaluationcriteria,manyofthese
studies have contributed to the efficient processing of a large amount of data.
However,whenitcomestoapplyingdataminingtoanalyzingservices,calculation
speed and prediction accuracy do not suffice; instead, algorithms and techniques
that are appropriate for a particular service must be adopted or developed.
Therefore, expertise in the service domain is crucial in applying data mining in
services. This book reveals how data mining can be applied to the service sector
within a variety of service-related examples. Understanding the compatible rela-
tion between the expertise in services and data mining techniques will provide
insights on the extended use of data mining in other service domains.
I would like to thank everyone who has supported me in the publishing of this
book. I would like to address my special thanks tothe authors of all the chapters,
who offered new ideas and valuable perspectives; staff members at Springer for
their continued guidance in the editing process, and the secretaries at Kansai
University Data Mining Laboratory. This work was supported by the program for
the Strategic Research Foundation at Private Universities from Ministry of
v
vi Preface
Education,Culture,Sports,ScienceandTechnology(MEXT),2009–2013.Finally,
I hope this book will stimulate interest in the relation between data mining tech-
nology and its application to other fields and provide important insights for many
researchers and practitioners involved in the service sector.
Osaka, October 2013 Katsutoshi Yada
Contents
Part I Fundamental Technologies Supporting Service Science
Data Mining for Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Katsutoshi Yada
Feature Selection Over Distributed Data Streams. . . . . . . . . . . . . . . . 11
Jacob Kogan
Learning Hidden Markov Models Using Probabilistic
Matrix Factorization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Ashutosh Tewari and Michael J. Giering
Dimensionality Reduction for Information Retrieval
Using Vector Replacement of Rare Terms . . . . . . . . . . . . . . . . . . . . . 41
Tobias Berka and Marian Vajteršic
Panel Data Analysis via Variable Selection
and Subject Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
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 . . . . . 79
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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Sujoy Roy, Ramin Homayouni, Michael W. Berry
and Andrey A. Puretskiy
vii
viii Contents
Text Mining of Business-Oriented Conversations at a Call Center . . . 111
Hironori Takeuchi and Takahira Yamaguchi
Part III Approach for New Services in Social Media
Scam Detection in Twitter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Xiaoling Chen, Rajarathnam Chandramouli
and Koduvayur P. Subbalakshmi
A Matrix Factorization Framework for Jointly Analyzing
Multiple Nonnegative Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . 151
Sunil Kumar Gupta, Dinh Phung, Brett Adams and Svetha Venkatesh
Recommendation Systems for Web 2.0 Marketing . . . . . . . . . . . . . . . 171
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. . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
Barnan Das, Narayanan C. Krishnan and Diane J. Cook
Change Detection from Heterogeneous Data Sources . . . . . . . . . . . . . 221
Tsuyoshi Idé
Interesting Subset Discovery and Its Application
on Service Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
Maitreya Natu and Girish Keshav Palshikar
Text Document Cluster Analysis Through Visualization
of 3D Projections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
Masaki Aono and Mei Kobayashi
Part I
Fundamental Technologies Supporting
Service Science