Data Clustering Using Kernel Based Algorithm

Abstract

In recent machine learning community, there is a trend of constructing a linear logarithm version of nonlinear version through the ‘kernel method’ for example kernel principal component analysis, kernel fisher discriminant analysis, support Vector Machines (SVMs), and the current kernel clustering algorithms. Typically, in unsupervised methods of clustering algorithms utilizing kernel method, a nonlinear mapping is operated initially in order to map the data into a much higher space feature, and then clustering is executed. A hitch of these kernel clustering algorithms is that the clustering prototype resides in increased features specs of dimensions and therefore lack intuitive and clear descriptions without utilizing added approximation of projection from the specs to the data as executed in the literature presented. This paper aims to utilize the ‘kernel method’, a novel clustering algorithm, founded on the conventional fuzzy clustering algorithm (FCM) is anticipated and known as kernel fuzzy c-means algorithm (KFCM). This method embraces a novel kernel-induced metric in the space of data in order to interchange the novel Euclidean matric norm in cluster prototype and fuzzy clustering algorithm still reside in the space of data so that the results of clustering could be interpreted and reformulated in the spaces which are original. This property is used for clustering incomplete data. Execution on supposed data illustrate that KFCM has improved performance of clustering and stout as compare to other transformations of FCM for clustering incomplete data.