Overlapping clustering based on kernel similarity metric

TL;DR

This paper introduces a novel overlapping clustering method using a kernel similarity metric, which improves cluster detection accuracy and estimates the number of clusters effectively, demonstrated on Iris and EachMovie datasets.

Contribution

It proposes a new overlapping clustering approach based on kernel similarity and a method to estimate the number of clusters using the Gram matrix.

Findings

Kernel similarity improves clustering precision, recall, and F-measure.

The method accurately estimates the number of overlapping clusters.

Experiments validate the effectiveness on Iris and EachMovie datasets.

Abstract

Producing overlapping schemes is a major issue in clustering. Recent proposed overlapping methods relies on the search of an optimal covering and are based on different metrics, such as Euclidean distance and I-Divergence, used to measure closeness between observations. In this paper, we propose the use of another measure for overlapping clustering based on a kernel similarity metric .We also estimate the number of overlapped clusters using the Gram matrix. Experiments on both Iris and EachMovie datasets show the correctness of the estimation of number of clusters and show that measure based on kernel similarity metric improves the precision, recall and f-measure in overlapping clustering.