Parallelized Kernel Patch Clustering

Abstract

Abstract. Kernel based clustering methods allow to unsupervised partition samples in feature space but have a quadratic computation time O(n 2 ) where n are the number of samples. Therefore these methods are generally ineligible for large datasets. In this paper we propose a metaalgorithm that performs parallelized clusterings of subsets of the samples and merges them repeatedly. The algorithm is able to use many Kernel based clustering methods where we mainly emphasize on Kernel Fuzzy C-Means and Relational Neural Gas. We show that the computation time of this algorithm is basicly linear, i.e. O(n). Further we statistically evaluate the performance of this meta-algorithm on a real-life dataset, namely the Enron Emails.

Evaluation of modified adaptive k-means segmentation algorithm

“…Faußer and Schwenker [17] proposed an algorithm that divides the samples into subsets to perform clustering in parallel, and merges the output repeatedly. In their proposed approach they used many kernel-based FCM clustering algorithms.…”