Interval fuzzy possibilistic c-means clustering algorithm on smart phone implement
Jin-Tsong Jeng, Chen‐Chia Chuang, Sheng-Chieh Chang · 2014
Clustering algorithms have been widely used in many different applications such as pattern recognition, data mining. It is unsupervised learning algorithm. At the same, the data sets of similarity partition belong to the same group; otherwise data sets divide other groups in the clustering algorithms. The interval fuzzy c-means (IFCM) clustering method was proposed to deal with symbolic interval data. However, it still has noisy and outliers problems. Hence, in this paper we propose interval fuzzy possibilistic c-means (IFPCM) clustering algorithm to overcome the IFCM clustering algorithm for the symbolic interval data clustering in noisy and outlier environments under smart phone. From the results of simulation shows that the proposed IFPCM clustering algorithm is implemented on windows mobile (smart) phone and demonstrated nice performance as expected.