TY - GEN
T1 - A noise-resistant fuzzy c means algorithm for clustering
AU - Chintalapudi, Krishna K.
AU - Kam, Moshe
N1 - Publisher Copyright:
© 1998 IEEE.
PY - 1998
Y1 - 1998
N2 - Probabilistic clustering techniques use the concept of memberships to describe the degree by which a vector belongs to a cluster. The use of memberships provides probabilistic methods with more realistic clustering than "hard" techniques. However, fuzzy schemes (like the fuzzy c means algorithm, FCM) are often sensitive to outliers. We review four existing algorithms, devised to reduce this sensitivity. These are: the noise cluster (NC) algorithm of Dave (1991), the possibilistic c means (PCM) scheme of Krishnapuram and Keller (1996), the least biased fuzzy clustering (LBFC) method of Beni and Liu (1994), and the fuzzy possibilistic c means algorithm of Pal et al. (1997). We then propose the new credibilistic fuzzy c means (CFCM) algorithm to improve on these methods. It uses a new variable, credibility of a vector, which measures the typicality of the vector to the whole data set. By taking credibility into account CFCM generates centroids which are less sensitive to outliers than other techniques, and closer to the centroids generated when the outliers are artificially removed.
AB - Probabilistic clustering techniques use the concept of memberships to describe the degree by which a vector belongs to a cluster. The use of memberships provides probabilistic methods with more realistic clustering than "hard" techniques. However, fuzzy schemes (like the fuzzy c means algorithm, FCM) are often sensitive to outliers. We review four existing algorithms, devised to reduce this sensitivity. These are: the noise cluster (NC) algorithm of Dave (1991), the possibilistic c means (PCM) scheme of Krishnapuram and Keller (1996), the least biased fuzzy clustering (LBFC) method of Beni and Liu (1994), and the fuzzy possibilistic c means algorithm of Pal et al. (1997). We then propose the new credibilistic fuzzy c means (CFCM) algorithm to improve on these methods. It uses a new variable, credibility of a vector, which measures the typicality of the vector to the whole data set. By taking credibility into account CFCM generates centroids which are less sensitive to outliers than other techniques, and closer to the centroids generated when the outliers are artificially removed.
UR - http://www.scopus.com/inward/record.url?scp=0031633290&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0031633290&partnerID=8YFLogxK
U2 - 10.1109/FUZZY.1998.686334
DO - 10.1109/FUZZY.1998.686334
M3 - Conference contribution
AN - SCOPUS:0031633290
SN - 078034863X
SN - 9780780348639
T3 - 1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence
SP - 1458
EP - 1463
BT - 1998 IEEE International Conference on Fuzzy Systems Proceedings - IEEE World Congress on Computational Intelligence
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1998 IEEE International Conference on Fuzzy Systems, FUZZY 1998
Y2 - 4 May 1998 through 9 May 1998
ER -