Fuzzy Clustering Algorithm Efficient Implementation Using Centre of Centres
C. Ramesh, K. Phalguna Rao, Gunamani Jena · International journal of intelligent engineering and systems · 2018
Clustering is a procedure of finding similar data items (patterns, documents etc.) and then group the similar data together.Items belongs to different clusters are dissimilar data items, generally cluster values are considered as 1 or 0. The clustering process is not appropriate for all the cases sometimes these values are less than one.In practical situations clusters are not crisp, then it is represented as fuzzy.In order to enhance the clustering rate, two appropriate clustering approaches: K-means clustering and Fuzzy C Means (FCM) are considered.These approaches are modified by minimizing the objective function known as squared error function.The experimental research was performed on the publicly available database (i.e.yeast dataset) to validate its clustering performance in terms of accuracy, specificity, sensitivity and execution time.Experimental outcome shows that the proposed technique improves the accuracy in clustering rate up to 1.5-35% compared to the existing methodologies FCM and k-means approaches.