scholarly journals Towards general semi-supervised clustering using a cognitive reinforcement K-Iteration fast learning artificial neural network (R-Kflann)

2010 ◽  
Author(s):  
Rina Tse
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supervised Clustering ◽  
Fast Learning ◽  
Artificial Neural

scholarly journals Neural Network Based Fuzzy C-MEANS Clustering Algorithm

2011 ◽  
pp. 100-104
Author(s):  
Suneetha Chittinen ◽  
Dr. Raveendra Babu Bhogapathi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Fast Learning ◽  
Presentation Sequence ◽  
Fuzzy C Means Clustering ◽  
Artificial Neural

In this paper, fuzzy c-means algorithm uses neural network algorithm is presented. In pattern recognition, fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms to group the high dimensional data into clusters. The proposed work involves two steps. First, a recently developed and Enhanced Kmeans Fast Leaning Artificial Neural Network (KFLANN) frame work is used to determine cluster centers. Secondly, Fuzzy C-means uses these cluster centers to generate fuzzy membership functions. Enhanced K-means Fast Learning Artificial Neural Network (KFLANN) is an algorithm which produces consistent classification of the vectors in to the same clusters regardless of the data presentation sequence. Experiments are conducted on two artificial data sets Iris and New Thyroid. The result shows that Enhanced KFLANN is faster to generate consistent cluster centers and utilizes these for elicitation of efficient fuzzy memberships.

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