Pattern Clustering using Soft-Computing Approaches

Agrawal, Mohit (2012) Pattern Clustering using Soft-Computing Approaches. BTech thesis.



Clustering is the process of partitioning or grouping a given set of patterns into disjoint clusters. This is done such that patterns in the same cluster are alike and patterns belonging to two dierent clusters are dierent. . Clustering Process can be divided into two parts
Cluster formation
Cluster validation
The most trivial K-means algorithm is rst implemented on the data set obtained from UCI machine repository. The comparison is extended to Fuzzy C-means algorithm where each data is a member of every cluster but with a certain degree known as membership value. Finally, to obtain the optimal value of K Genetic K-means algorithm in implemented in which GA nds the value of K as generation evolves.The
ecieny of the three algorithms can be judged on the two measuring index such as :
the silhouette index and Davies-Bouldin Index .

Item Type:Thesis (BTech)
Uncontrolled Keywords:FCM:Fuzzy C-Means, GKA:Genetic K-Means Algorithm,CPU:Central Processing Unit
Subjects:Engineering and Technology > Computer and Information Science > Data Mining
Divisions: Engineering and Technology > Department of Computer Science
ID Code:3744
Deposited By:Mr Mohit Agrawal
Deposited On:05 Jun 2012 12:11
Last Modified:05 Jun 2012 12:11
Supervisor(s):Rath, S K

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