Hybrid data clustering approach using K-Means and Flower Pollination Algorithm

TL;DR

This paper introduces a hybrid clustering method combining K-Means and Flower Pollination Algorithm to improve clustering quality and avoid local optima, demonstrating superior results over individual methods on multiple datasets.

Contribution

A novel hybrid clustering algorithm (FPAKM) that integrates global optimization with local refinement, enhancing clustering performance.

Findings

FPAKM outperforms K-Means and FPA on eight datasets.

The hybrid approach achieves better clustering accuracy.

Experimental results validate the effectiveness of FPAKM.

Abstract

Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Means algorithm suffers from initialization, hence trapped in local optima. Flower Pollination Algorithm (FPA) is the global optimization technique, which avoids trapping in local optimum solution. In this paper, a novel hybrid data clustering approach using Flower Pollination Algorithm and K-Means (FPAKM) is proposed. The proposed algorithm results are compared with K-Means and FPA on eight datasets. From the experimental results, FPAKM is better than FPA and K-Means.