A Competency Analysis of Ant Colony Optimization and K-Harmonic means Clustering Algorithm
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Abstract
Clustering is an important technique that has been studied in various fields with many applications such as image processing, marketing, data mining and information retrieval. Recently, the various algorithms inspired by nature are used for clustering. These swarm intelligence based clustering models and algorithms have advantages in many aspects. This paper focuses on the behavior of clustering procedures in a new approach called ant based clustering algorithm and K-harmonic means clustering algorithm. The two algorithms were evaluated in a number of well- known benchmark data sets. Empirical results clearly show that ant clustering algorithm (ACOC) performs well compared to another technique called K-Harmonic means clustering algorithm (KHM).
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Keywords: biological algorithms, data mining and clustering techniques
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