Improving K-means clustering algorithm with the intelligent water drops (IWD) algorithm

Hamed Shah-Hosseini

International Journal of Data Mining, Modelling and Management, 2013, vol. 5, issue 4, 301-317

Abstract: In this paper, the K-means algorithm for data clustering is improved by the swarm-based nature-inspired optimisation algorithm, the intelligent water drops (IWD) algorithm. The K-means algorithm is an iterative algorithm in which the number of clusters is given in advance. Although the K-means is fast to converge, it is sensitive to the initial conditions. As a result, it is often trapped in local optimums. The IWD algorithm, which mimics the actions and reactions between natural water drops in real rivers, is modified to implicitly embed in itself the main processes of the K-means algorithm. The modified algorithm called IWD-KM is tested with several well-known datasets for clustering, and its performance is compared with the K-means algorithm. The experimental results show the superiority of the proposed IWD-KM algorithm.

Keywords: K-means clustering; metaheuristics; intelligent water drops; IWD algorithm; chaos. (search for similar items in EconPapers)
Date: 2013
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