Document clustering by fuzzy c-mean algorithm

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 wh...

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Veröffentlicht in:2010 2nd International Conference on Advanced Computer Control Jg. 1; S. 239 - 242
Hauptverfasser: Thaung Thaung Win, Lin Mon
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.03.2010
Schlagworte:
Algorithm design and analysis
Clulster Validity
Clustering algorithms
Clustering methods
Document Clustering
Fuzzy c-mean algorithm
Fuzzy sets
Fuzzy systems
Information retrieval
Navigation
Partitioning algorithms
PBMindex
Spatial databases
Topology
ISBN:1424458455, 9781424458455
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
ISBN:1424458455
9781424458455
DOI:10.1109/ICACC.2010.5487022