COMPARISON OF ALGORITHMS FOR CLUSTERING INCOMPLETE DATA

被引：4

作者：

Matyja, Artur ^{[1
]}

Siminski, Krzysztof ^{[1
]}

机构：

[1] Silesian Tech Univ, Inst Informat, Ul Akad 16, PL-44100 Gliwice, Poland

来源：

FOUNDATIONS OF COMPUTING AND DECISION SCIENCES | 2014年 / 39卷 / 02期

关键词：

clustering; incomplete data; missing value; marginalisation; imputation; IFCM; OCS; NPS; NCS;

D O I：

10.2478/fcds-2014-0007

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The missing values are not uncommon in real data sets. The algorithms and methods used for the data analysis of complete data sets cannot always be applied to missing value data. In order to use the existing methods for complete data, the missing value data sets are preprocessed. The other solution to this problem is creation of new algorithms dedicated to missing value data sets. The objective of our research is to compare the preprocessing techniques and specialised algorithms and to find their most advantageous usage.

引用

页码：107 / 127

页数：21

共 27 条

[1] Acuna E, 2004, ST CLASS DAT ANAL, P639
[2] Alcala-Fdez J, 2011, J MULT-VALUED LOG S, V17, P255
[3] Validity-guided (re)clustering with applications to image segmentation
Bensaid, AM
Hall, LO
Bezdek, JC
Clarke, LP
Silbiger, ML
Arrington, JA
Murtagh, RF
[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 1996, 4 (02) : 112 - 123
[4] ALTERNATIVE APPROACHES TO MISSING VALUES IN DISCRIMINANT-ANALYSIS
CHAN, LS
GILMAN, JA
DUNN, OJ
[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1976, 71 (356) : 842 - 844
[5] CZOGALA E, 2000, SERIES FUZZINESS SOF
[6] MAXIMUM LIKELIHOOD FROM INCOMPLETE DATA VIA EM ALGORITHM
DEMPSTER, AP
LAIRD, NM
RUBIN, DB
[J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL, 1977, 39 (01): : 1 - 38
[7] Dunn J. C., 1973, CYBERNET SYST, V3, P32
[8] Ghahramani Z., 1995, 1509 CBCL MIT AI LAB
[9] Grzymala-Busse J., 2004, P IPMU 2004 10 INT C, P923
[10] Grzymala-Busse JW, 2005, DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, P37, DOI 10.1007/0-387-25465-X_3

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