Fuzzy Mean Point Clustering using K-means algorithm for implementing the movecentroid function code
Journal Title: INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY - Year 2013, Vol 4, Issue 1
Abstract
The paper focus on combination of K-Means algorithm for Fuzzy Mean Point Clustering Neural Network (FMPCNN). The algorithm is implemented in JAVA program code for implementing the movecentroid function code into FMPCNN. Here we have provided movecentroid’s output to Fuzzy clustering as criteria, movecentroid is the base function of K-means algorithm as in Fuzzy Mean Point Clustering Neural Network (FMPCNN) algorithm, calculation of cluster based on pre-defined criteria and scope is done. In the experiment we have used four datasets and observed results in nano seconds there is huge difference in output as time is reduced for Fuzzy Min-Max code execution of fuzzy calculations of clustering.
Authors and Affiliations
Ganeshchandra Narharrao Shinde, Inamdar S. A. , Narangale S. M.
Keywords
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- EP ID EP649887
- DOI 10.24297/ijct.v4i1b.3059
- Views 109
- Downloads 0
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