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

Authors: Hamed Shah-Hosseini

Addresses: Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G.C., Tehran, Iran

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.

DOI: 10.1504/IJDMMM.2013.057681

International Journal of Data Mining, Modelling and Management, 2013 Vol.5 No.4, pp.301 - 317

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 18 Nov 2013 *

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