DocumentCode
2677462
Title
Document clustering by fuzzy c-mean algorithm
Author
Win, Thaung Thaung ; Mon, Lin
Author_Institution
Univ. of Comput. Studies Mandalay, UCSM, Yangon, Myanmar
Volume
1
fYear
2010
fDate
27-29 March 2010
Firstpage
239
Lastpage
242
Abstract
Clustering documents enable the user to have a good overall view of the information contained in the documents. Most classical clustering algorithms assign each data to exactly one cluster, thus forming a crisp partition of the given data, but fuzzy clustering allows for degrees of membership, to which a data belongs to different clusters. In this system, documents are clustered by using fuzzy c-means (FCM) clustering algorithm. FCM clustering is one of well-know unsupervised clustering techniques. However FCM algorithm requires the user to pre-define the number of clusters and different values of clusters corresponds to different fuzzy partitions. So the validation of clustering result is needed. PBM index and F-measure are used for cluster validity.
Keywords
document handling; fuzzy set theory; pattern clustering; F-measurement; PBM index; document clustering; fuzzy c-mean algorithm; fuzzy partitions; unsupervised clustering techniques; Algorithm design and analysis; Clustering algorithms; Clustering methods; Fuzzy sets; Fuzzy systems; Information retrieval; Navigation; Partitioning algorithms; Spatial databases; Topology; Clulster Validity; Document Clustering; Fuzzy c-mean algorithm; PBMindex;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Computer Control (ICACC), 2010 2nd International Conference on
Conference_Location
Shenyang
Print_ISBN
978-1-4244-5845-5
Type
conf
DOI
10.1109/ICACC.2010.5487022
Filename
5487022
Link To Document