Data Clustering using a Hybrid of Fuzzy C-Means and Quantum-behaved Particle Swarm Optimization
This work addresses the issue of suboptimal clustering solutions in real-world applications, but it is incremental as it combines existing methods.
The paper tackled the problem of fuzzy clustering for multidimensional data by hybridizing Fuzzy C-Means with Quantum-behaved Particle Swarm Optimization, resulting in QPSO FCM achieving comparable or superior performance on UCI datasets, as measured by indices like F-Measure and Accuracy.
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.