"Low-complexity fuzzy relational clustering algorithms for Web mining"
Eugene Garfield
garfield at CODEX.CIS.UPENN.EDU
Wed Jul 16 16:27:50 EDT 2003
R. Krishnapuram: kraghura at in.ibm.edu
A. Joshi :joshi at cs.umbc.edu
FULL TEXT AVAILABLE AT :
http://163.17.28.170/clustering/CLUSTER/IEEE/2001/JOURNAL/Low-complexity%20f
uzzy%20relational%20clustering%20algorithms%20for%20Web%20mining.pdf
TITLE Low-complexity fuzzy relational clustering
algorithms for Web mining
Krishnapuram R, Joshi A, Nasraoui O, Yi LY
IEEE TRANSACTIONS ON FUZZY SYSTEMS 9 (4): 595-607 AUG 2001
Document type: Article Language: English
Cited References: 54 Times Cited: 0
Abstract:
This paper presents new algorithms - fuzzy c-medoids (FCMdd) and robust
fuzzy c-medoids (RFCMdd) - for fuzzy clustering of relational data. The
objective functions are based on selecting c representative objects
(medoids) from the data set in such a way that the total fuzzy dissimilarity
within each cluster is minimized.
A comparison of FCMdd with the well-known relational fuzzy c-means algorithm
(RFCM) shows that FCMdd is more efficient. We present several applications
of these algorithms to Web mining, including Web document clustering,
snippet clustering, and Web access log analysis.
Author Keywords:
document clustering, fuzzy relational clustering, snippet clustering, user
access patterns, user profiles, Web mining
KeyWords Plus:
WORLD-WIDE-WEB, C-MEANS, RECOMMENDATIONS, INFORMATION
Addresses:
Krishnapuram R, Indian Inst Technol, IBM India Res Lab, New Delhi 110016,
India
Indian Inst Technol, IBM India Res Lab, New Delhi 110016, India
Colorado Sch Mines, Dept Math & Comp Sci, Golden, CO 80401 USA
Univ Maryland, Dept Comp Sci & Elect Engn, Baltimore, MD 21250 USA
Univ Memphis, Dept Elect Engn, Memphis, TN 38152 USA
BoldTech Syst Inc, Denver, CO 80202 USA
Publisher:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, NEW YORK
IDS Number:
464HD
ISSN:
1063-6706
Cited Author Cited Work Volume Page Year
COMMUN ACM 43 2000
ABIDI J P 7 IEEE INT WORKSH 20 1997
AGRAWAL R P 20 INT C VER LARG 487 1994
ARMSTRONG R P S INF GATH HET DIS 6 1995
AROCENA GO PROC INT CONF DATA 24 1998
BAJCSY P IEEE T PATTERN ANAL 20 1011 1998
BENI G IEEE T PATTERN ANAL 16 954 1994
BEZDEK JC PATTERN RECOGNITION 1981
CHEN J KNOWLEDGE MANAGEMENT 163 2000
CHEN MS IEEE T KNOWL DATA EN 10 209 1998
COOLEY R PROC INT C TOOLS ART 558 1997
DAVE RN IEEE T FUZZY SYST 5 270 1997
DIDAY E REVUE STATISTIQUE AP 19 19 1971
FINK J PERSONALIZED HYPERME 1997
FU KS SYNTACTIC PATTERN RE 1982
GOWDA KC IEEE T SYST MAN CYB 22 368 1992
GUHA S P ACM SIGMOD INT C M 73 1998
HATHAWAY RJ IEEE T FUZZY SYST 1 195 1993
HATHAWAY RJ PATTERN RECOGN 27 429 1994
HATHAWAY RJ PATTERN RECOGN 22 205 1989
JARCCARD P B SOC VARD SCI NAT 1908
JOACHIMS T P 15 INT JOINT C ART 770 1997
JONES KS J DOC 28 11 1972
JOSHI A P 7 IEEE INT WORKSH 101 1997
JOSHI A P WORKSH WEB INF DAT 17 1998
KAUFMAN J FINDING GROUPS DATA 1990
KAUFMAN J STAT DATA ANAL BASED 405 1987
KIM JW PATTERN RECOGN LETT 17 633 1996
KRISHNAPURAM R IEEE T FUZZY SYST 4 385 1996
KRISHNAPURAM R IEEE T FUZZY SYST 1 98 1993
MIYAMOTO S FUZZY SETS INFORMATI 1990
NASRAOUI O P 8 INT WORLD WID WE 40 1999
NASRAOUI O P AM FUZZ INF PROC S 217 1997
NASRAOUI O P N AM FUZZ INF SOC 705 1999
NG RT P 20 INT C VER LARG 144 1994
PAZZANI M PROC INT C TOOLS ART 492 1995
PERKOWITZ M ARTIF INTELL 118 245 2000
PERKOWITZ M P AAAI IAAI 98 727 1998
RAMKUMAR GD B IEEE COMPUTER SOC 21 9 1998
ROUBENS M FUZZY SETS SYSTEMS 1 239 1978
ROUSSEEUW PJ ROBUST REGRESSION OU 1987
RUNKLER TA IEEE T FUZZY SYST 7 377 1999
RUSPINI E INFORMATION SCI 2 319 1970
SALTON G INTRO MODERN INFORMA 1983
SEN S P IEEE INT C FUZZ SY 1411 1998
SHRADANAND U P CHI 95 C HUM FACT 1995
SNEATH PHA NUMERICAL TAXONOMY P 1973
SONBATY YE IEEE T FUZZY SYST 6 195 1998
TERVEEN L COMMUN ACM 40 59 1997
THEODORIDIS S PATTERN RECOGNITION 1999
WINDHAM MP J CLASSIF 2 157 1985
ZAIANE O P INT WORKSH WEB INF 9 1998
ZAIANE OR P IEEE INT FORUM RES 19 1998
ZAMIR O P 21 ANN INT ACM SIG 46 1998
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