Data Clustering using a Hybrid of Fuzzy C-Means and Quantum-behaved Particle Swarm Optimization

TL;DR

This paper introduces a hybrid clustering method combining Quantum-behaved Particle Swarm Optimization with Fuzzy C-Means, improving global search and clustering accuracy on real-world datasets.

Contribution

The paper proposes a novel hybrid algorithm integrating QPSO with FCM, enhancing clustering performance and avoiding local optima in multidimensional data.

Findings

QPSO FCM outperforms traditional methods in accuracy and clustering quality.

The hybrid approach effectively avoids local optima in complex datasets.

Experimental results show superior or comparable performance across multiple metrics.

Abstract

Fuzzy clustering has become a widely used data mining technique and plays an important role in grouping, traversing and selectively using data for user specified applications. The deterministic Fuzzy C-Means (FCM) algorithm may result in suboptimal solutions when applied to multidimensional data in real-world, time-constrained problems. In this paper the Quantum-behaved Particle Swarm Optimization (QPSO) with a fully connected topology is coupled with the Fuzzy C-Means Clustering algorithm and is tested on a suite of datasets from the UCI Machine Learning Repository. The global search ability of the QPSO algorithm helps in avoiding stagnation in local optima while the soft clustering approach of FCM helps to partition data based on membership probabilities. Clustering performance indices such as F-Measure, Accuracy, Quantization Error, Intercluster and Intracluster distances are reported for competitive techniques such as PSO K-Means, QPSO K-Means and QPSO FCM over all datasets considered. Experimental results indicate that QPSO FCM provides comparable and in most cases superior results when compared to the others.