Transactions on Rough Sets XIV

241 Pages·2011·2.91 MB·English

by  Hidenao Abe

Lecture Notes in Computer Science 6600
CommencedPublicationin1973
FoundingandFormerSeriesEditors:
GerhardGoos,JurisHartmanis,andJanvanLeeuwen
EditorialBoard
DavidHutchison
LancasterUniversity,UK
TakeoKanade
CarnegieMellonUniversity,Pittsburgh,PA,USA
JosefKittler
UniversityofSurrey,Guildford,UK
JonM.Kleinberg
CornellUniversity,Ithaca,NY,USA
FriedemannMattern
ETHZurich,Switzerland
JohnC.Mitchell
StanfordUniversity,CA,USA
MoniNaor
WeizmannInstituteofScience,Rehovot,Israel
OscarNierstrasz
UniversityofBern,Switzerland
C.PanduRangan
IndianInstituteofTechnology,Madras,India
BernhardSteffen
TUDortmundUniversity,Germany
MadhuSudan
MicrosoftResearch,Cambridge,MA,USA
DemetriTerzopoulos
UniversityofCalifornia,LosAngeles,CA,USA
DougTygar
UniversityofCalifornia,Berkeley,CA,USA
MosheY.Vardi
RiceUniversity,Houston,TX,USA
GerhardWeikum
MaxPlanckInstituteforInformatics,Saarbruecken,Germany
James F. Peters Andrzej Skowron
Hiroshi Sakai Mihir Kumar Chakraborty
Dominik Slezak Aboul Ella Hassanien
William Zhu (Eds.)
Transactions on
Rough Sets XIV
1 3
Editors-in-Chief
JamesF.Peters
UniversityofManitoba,Winnipeg,MB,Canada
E-mail:[email protected]
AndrzejSkowron
UniversityofWarsaw,Poland
E-mail:[email protected]
GuestEditors
HiroshiSakai
KyushuInstituteofTechnology,Tobata,Kitakyushu,Japan
E-mail:[email protected]
MihirKumarChakraborty
JadavpurUniversityandIndianStatisticalInstitute,Calcutta,India
E-mail:[email protected]
DominikSlezak
UniversityofWarsaw,Poland
E-mail:[email protected]
AboulEllaHassanien
CairoUniversity,Orman,Giza,Egypt
E-mail:[email protected]
WilliamZhu
ZhangzhouNormalUniversity,Zhangzhou,Fujian,China
E-mail:[email protected]
ISSN0302-9743(LNCS) e-ISSN1611-3349(LNCS)
ISSN0302-9743(TRS) e-ISSN1611-3349(TRS)
ISBN978-3-642-21562-9 e-ISBN978-3-642-21563-6
DOI10.1007/978-3-642-21563-6
SpringerHeidelbergDordrechtLondonNewYork
LibraryofCongressControlNumber:2011929861
CRSubjectClassification(1998):F.4.1,F.1.1,H.2.8,I.5,I.4,I.2
©Springer-VerlagBerlinHeidelberg2011
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Preface
Volume XIV of the Transactions on Rough Sets includes extensions of papers
presentedatthe 12thInternationalConferenceonRoughSets,FuzzySets,Data
Mining and Granular Computing (RSFDGrC 2009)held in the Indian Institute
ofTechnology,Delhi,India,andpublishedintheLectureNotesinArtificialIntel-
ligence volume 5908.The authorsof14 paperswere invitedto prepareextended
manuscripts. Each submission was peer-reviewed by three reviewers. After two
rounds of reviews, 10 articles were accepted for publication.
Thecontentsofthisspecialissuerefertoboththeoryandpractice.Thetopics
include various rough set generalizations in combination with formal concept
analysis,latticetheory,fuzzysetsandbelieffunctions,roughandfuzzyclustering
techniques, as well as applications to gene selection,Web page recommendation
systems, facial recognition, and temporal pattern detection. In addition, this
publicationcontainsaregulararticleonroughmultisetanditsmultisettopology.
Theeditorsofthisspecialissuewouldliketoexpressgratitudetotheauthors
ofallsubmitted papers.Specialthanksaredue to the followingreviewers:Hide-
naoAbe,SuruchiChawla,ZiedElouedi,HomaFashandi,DarylHepting,Andrzej
Janusz,NaJiao,MichiroKondo,PawanLingras,DuoqianMiao,E.K.R.Nagara-
jan,BidyutKr.Patra,SheelaRamanna,andPulakSamanta.Wewouldalsolike
to acknowledge Sheela Ramanna for technical management of the TRS online
system.The editors andauthors of this volume extend their gratitude to Alfred
Hofmann, Anna Kramer, Ursula Barth, Christine Reiss, and the LNCS staff at
Springer for their support in making this volume of the TRS possible.
The Editors-in-Chief were supported by the Ministry of Science and Higher
Education of the Republic of Poland research grants N N516 368334, N N516
077837,and the Natural Sciences and Engineering Research Council of Canada
(NSERC) research grant 185986, Canadian Network of Excellence (NCE), and
a Canadian Arthritis Network (CAN) grant SRI-BIO-05.
February 2011 Hiroshi Sakai
Mihir K. Chakraborty
Dominik S´le¸zak
Aboul E. Hassanien
William Zhu
James F. Peters
Andrzej Skowron
LNCS Transactions on Rough Sets
The Transactions on Rough Sets series has as its principal aim the fostering of
professional exchanges between scientists and practitioners who are interested
in the foundations and applications of rough sets. Topics include foundations
and applications of rough sets as well as foundations and applications of hybrid
methodscombiningroughsetswithotherapproachesimportantforthedevelop-
ment of intelligent systems. The journal includes high-quality research articles
accepted for publication on the basis of thorough peer reviews. Dissertations
and monographs up to 250 pages that include new research results can also be
considered as regular papers. Extended and revised versions of selected papers
from conferences can also be included in regular or special issues of the journal.
Editors-in-Chief: James F. Peters, Andrzej Skowron
Managing Editor: Sheela Ramanna
Technical Editor: Marcin Szczuka
Editorial Board
Mohua Banerjee Ewa Orl(cid:3)owska
Jan Bazan Sankar K. Pal
Gianpiero Cattaneo Lech Polkowski
Mihir K. Chakraborty Henri Prade
Davide Ciucci Sheela Ramanna
Chris Cornelis Roman Sl(cid:3)owin´ski
Ivo Du¨ntsch Jerzy Stefanowski
Anna Gomolin´ska Jaros(cid:3)law Stepaniuk
Salvatore Greco Zbigniew Suraj
Jerzy W. Grzymal(cid:3)a-Busse Marcin Szczuka
Masahiro Inuiguchi Dominik S´le¸zak
Jouni J¨arvinen Roman S´winiarski
Richard Jensen Shusaku Tsumoto
Boz˙ena Kostek Guoyin Wang
Churn-Jung Liau Marcin Wolski
PawanLingras Wei-Zhi Wu
Victor Marek Yiyu Yao
Mikhail Moshkov Ning Zhong
Hung Son Nguyen Wojciech Ziarko
Table of Contents
Evaluatinga TemporalPatternDetectionMethod for Finding Research
Keys in BibliographicalData ...................................... 1
Hidenao Abe and Shusaku Tsumoto
High Scent Web Page Recommendations Using Fuzzy Rough Set
Attribute Reduction.............................................. 18
Punam Bedi and Suruchi Chawla
A Formal Concept Analysis Approach to Rough Data Tables .......... 37
Bernhard Ganter and Christian Meschke
Rough Multiset and Its Multiset Topology .......................... 62
K.P. Girish and Sunil Jacob John
A Rough Set Exploration of Facial Similarity Judgements ............. 81
Daryl H. Hepting, Richard Spring, and Dominik S´lezak
(cid:2)
Evolutionary Tolerance-Based Gene Selection in Gene Expression
Data ........................................................... 100
Na Jiao
New Approach in Defining Rough Approximations ................... 119
E.K.R. Nagarajan and D. Umadevi
Tolerance Rough Set Theory BasedData Summarizationfor Clustering
Large Datasets .................................................. 139
Bidyut Kr. Patra and Sukumar Nandi
Projected Gustafson-Kessel Clustering Algorithm and Its
Convergence..................................................... 159
Charu Puri and Naveen Kumar
Generalized Rough Sets and Implication Lattices..................... 183
Pulak Samanta and Mihir Kumar Chakraborty
Classification with Dynamic Reducts and Belief Functions............. 202
Salsabil Trabelsi, Zied Elouedi, and Pawan Lingras
Author Index.................................................. 235
Evaluating a Temporal Pattern Detection
Method for Finding Research Keys in
Bibliographical Data
Hidenao Abe and Shusaku Tsumoto
Department of Medical Informatics, ShimaneUniversity,
School of Medicine
89-1 Enya-cho,Izumo, Shimane 693-8501, Japan
[email protected], [email protected]
Abstract. Accordingtotheaccumulationoftheelectricallystoreddoc-
uments, acquisition of valuable knowledge with remarkable trends of
technical terms has drawn the attentions as the topic in text mining.
In order to support for discovering key topics appeared as key terms in
suchtemporaltextualdatasets,weproposeamethodbasedontemporal
patternsinseveraldata-drivenindicesfortextmining.Themethodcon-
sists ofan automatic termextraction method ingiven documents,three
importanceindices,temporalpatternextractionbyusingtemporalclus-
tering, and trend detection based on linear trends of their centroids.
Empirical studiesshow that thethreeimportance indicesare applied to
thetitlesoftwoacademicconferencesaboutartificialintelligencefieldas
the sets of documents. After extracting the temporal patterns of auto-
matically extracted terms, we discuss the trends of the terms including
therecent burst words among thetitles of theconferences.
Keywords: Text Mining, Trend Detection, TF-IDF, Jaccard’s Match-
ing Coefficient, Temporal Clustering, Linear Regression.
1 Introduction
The development of information systems in every field such as business, aca-
demics, and medicine in recent years have been enabled to store huge amount
of various type data. Especially, document data Accumulation is advanced to
documentdataby notthe exceptionbut variousfields.Document dataprovides
valuablefindingsto notonly domainexpertsbutalsonoviceuser.Forsuchelec-
trical documents, it becomes more important to sense emergent targets about
researchers, decision makers, marketers by using temporal text mining tech-
niques [1]. In order to realize such detection, emergent term detection (ETD)
methods have been developed [2,3].
However,becausethefrequencyofwordswasusedinearliermethods,theirde-
tectionsweredifficultaslongasthewordthatbecameanobjectdidnotappear.
Besides, emergent or new concepts are usually appeared as new combination of
multiplewords,coinagescreatedbyanauthor,andwordswithdifferentspellings
J.F.Petersetal.(Eds.):TransactionsonRoughSetsXIV,LNCS6600,pp.1–17,2011.
(cid:2)c Springer-VerlagBerlinHeidelberg2011
2 H. Abeand S. Tsumoto
of currentwords.Most conventionalmethods did not consider above-mentioned
natures of terms and importance indices separately. This causes difficulties in
text mining applications, such as limitations on the extensionality of time di-
rection, time consuming post-processing, and generality expansions. For this
reason, conventional ETD methods have been developed for detecting the par-
ticular state of appeared words or/and phrases1 without considering any effect
nor similarity between the keywords.
After considering these problems, we focus on temporal behaviors of impor-
tance indices of terms and their temporal patterns. Temporal behaviors of the
importance indices of extracted phrases are paid attention so that a specialist
mayrecognizeemergenttermsand/orsuchfields.Inordertodetectvarioustem-
poralpatterns of behaviorsofterms in the sets ofdocuments, we haveproposed
a method to identify the remarkable terms as continuous changes of multiple
metrics of the terms [4]. Furthermore, we improved this method by clustering
the temporal behaviors of the index values of each terms for extracting similar
terms at the same time.
In this paper, we describe an integrated method for detecting trends of tech-
nical terms by combining automatic term extraction methods, importance in-
dices of the terms, and temporal clustering in Section 3. After implementing
this frameworkwith the three importanceindices,we performeda casestudy to
extract remarkable temporal patterns of technical terms on the titles of AAAI
and IJCAI in Section 4. By regarding these results, we evaluate the degree of
the clustersofknownemergenttermsthatbursttheir appearancefrequenciesin
recent years. Finally, in Section 6, we summarize this paper.
2 Related Work
Textminingisagenericframeworktofindoutvaluableinformationfromtextual
databyusingsomestatisticalmethods.Findingkeywordsinthesetofdocument
is one of the useful approaches in text mining. Based on a corpus, by using
clusteringmethods,theprocesstofindoutkeywordswithsimilartermsiscalled
term clustering [5]. For this approach, rough set theory can be also applied as
vocabularymining[6].However,suchconventionaltermclusteringmethodshave
not treated temporal information.
There exist some conventional studies on the detection of emergent trend
of terms/topics/themes in textual data. As the first step, Lent et. al [2] pro-
posedamethodforfindingtemporaltrendsofwords.Then,byapplyingvarious
metrics such as frequency [7], n-gram [8], and tf-idf [9], researchers developed
some emergent term detection (ETD) methods [3]. The methods developed in
[10,11] suggested for finding emergent theme patterns on the basis of a finite
state machine by using Hidden Markov Model (HMM) as one of the advanced
ETD method. Topic modeling [12] is a related method from the viewpoint of
temporal text analysis. In these methods, researchers consider the changes in
1 Wecalltheseimportantwordsandphrasesinacorpusas‘keywords’inthisarticle.In
addition,wordsandphrasesincludingthekeywordsinthecorpusarecalled‘terms’.
Evaluating a Method for Finding Research Keysin Bibliographical Data 3
eachparticular index of the terms rather,andthey considerthe emergenttrend
of terms as a discrete status based on appearance of the words.
In conventional studies on the detection of emergent words and/or phrases
in documents such as Web pages and particular electronic message boards, re-
searchers did not explicitly treat the trends of the calculated indices of words
and/orphrases.However,basedontwodifferenttechniques,weconsideraframe-
work for detecting temporal trends of phrases that consist of from two to nine
words.Wehavefocusedonshortphrasesbecauseaconsiderablylongphrasemay
be a pattern including grammatical structure and anonymous words, as shown
in [11].
As for examining the linear trends of the importance indices in temporal set
of documents, we detected the two kinds of trends in our previous study [4,13].
In the previous studies, we can find both of emergent and subsiding technical
phrases based on the trends of technical phrases. We used the degree based on
the linear regression technique and the intercept for y-axis for ranking the two
trends, however, some emergent phrases appears different behaviors based on
the valuesofthe importanceindicescomparedtothe composedindices byusing
PCA (Principal Component Analysis) [14]. In addition, the ranking lists of the
emergent and subsiding remain a difficulty to understand the similarity among
the listed terms. In order to overcome the difficulty, we introduced temporal
patternextractionphasetothepreviouswork.Thisprovidesanabstractedlayer
for understanding the meanings of the groups of similar terms on the basis of
temporal behaviors of terms, which are calculated as values of an importance
index.
3 An Integrated Method for Detecting Remarkable
Trends of Technical Terms as Temporal Patterns of
Importance Indices
In this section, we describe the difference between conventional ETD methods
and our proposal; detecting continuous temporal patterns of terms in temporal
sets of documents.
As illustratedin Fig.1, in orderto find remarkabletemporaltrends ofterms,
we developed a framework for detecting various temporal trends of technical
terms by using multiple importance indices consisting of the following four
components:
1. Technical term extraction in a corpus
2. Importance indices calculation
3. Temporal pattern extraction
4. Trend detection
Therearesomeconventionalmethodsfordetectingtemporaltrendsofkeywords
in a corpus on the basis of each particular importance index. Although these
methodscalculateeachindexinordertodetectimportantkeywords,information