TY - GEN
T1 - Fuzzifying clustering algorithms
T2 - 6th Mexican International Conference on Artificial Intelligence, MICAI 2007
AU - Levner, Eugene
AU - Pinto, David
AU - Rosso, Paolo
AU - Alcaide, David
AU - Sharma, R. R.K.
PY - 2007
Y1 - 2007
N2 - Among various document clustering algorithms that have been proposed so far, the most useful are those that automatically reveal the number of clusters and assign each target document to exactly one cluster. However, in many real situations, there not exists an exact boundary between different clusters. In this work, we introduce a fuzzy version of the MajorClust algorithm. The proposed clustering method assigns documents to more than one category by taking into account a membership function for both, edges and nodes of the corresponding underlying graph. Thus, the clustering problem is formulated in terms of weighted fuzzy graphs. The fuzzy approach permits to decrease some negative effects which appear in clustering of large-sized corpora with noisy data.
AB - Among various document clustering algorithms that have been proposed so far, the most useful are those that automatically reveal the number of clusters and assign each target document to exactly one cluster. However, in many real situations, there not exists an exact boundary between different clusters. In this work, we introduce a fuzzy version of the MajorClust algorithm. The proposed clustering method assigns documents to more than one category by taking into account a membership function for both, edges and nodes of the corresponding underlying graph. Thus, the clustering problem is formulated in terms of weighted fuzzy graphs. The fuzzy approach permits to decrease some negative effects which appear in clustering of large-sized corpora with noisy data.
UR - http://www.scopus.com/inward/record.url?scp=38349029222&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-76631-5_78
DO - 10.1007/978-3-540-76631-5_78
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AN - SCOPUS:38349029222
SN - 9783540766308
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 821
EP - 830
BT - MICAI 2007
PB - Springer Verlag
Y2 - 4 November 2007 through 10 November 2007
ER -