Abstract
Nature-inspired algorithms are based on the concepts of self-organization and complex biological systems. They have been designed by researchers and scientists to solve complex problems in various environmental situations by observing how naturally occurring phenomena behave. The introduction of nature-inspired algorithms has led to new branches of study such as neural networks, swarm intelligence, evolutionary computation, and artificial immune systems. Particle swarm optimization (PSO), social spider optimization (SSO), and other nature-inspired algorithms have found some success in solving clustering problems but they may converge to local optima due to the lack of balance between exploration and exploitation. In this paper, we propose a novel implementation of SSO, namely social spider optimization for data clustering using single centroid representation and enhanced mating operation (SSODCSC) in order to improve the balance between exploration and exploitation. In SSODCSC, we implemented each spider as a collection of a centroid and the data instances close to it. We allowed non-dominant male spiders to mate with female spiders by converting them into dominant males. We found that SSODCSC produces better values for the sum of intra-cluster distances, the average CPU time per iteration (in seconds), accuracy, the F-measure, and the average silhouette coefficient as compared with the K-means and other nature-inspired techniques. When the proposed algorithm is compared with other nature-inspired algorithms with respect to Patent corpus datasets, the overall percentage increase in the accuracy is approximately 13%. When it is compared with other nature-inspired algorithms with respect to UCI datasets, the overall percentage increase in the F-measure value is approximately 10%. For completeness, the best K cluster centroids (the best K spiders) returned by SSODCSC were specified. To show the significance of the proposed algorithm, we conducted a one-way ANOVA test on the accuracy values and the F-measure values returned by the clustering algorithms.
Highlights
Data clustering is one of the most popular unsupervised classification techniques in data mining
We investigated the performance of social spider optimization (SSO) for data clustering using a single centroid representation and enhanced mating operation
The proposed algorithm and the algorithms used in the comparison were implemented in the Java Run Time Environment, version 1.7.0.51, and the experiments were run on Intel Xeon CPU E3 1270 v3 with a 3.50-GHz processor with a 160 GB RAM
Summary
Data clustering is one of the most popular unsupervised classification techniques in data mining. It rearranges the given data instances into groups such that the similar data. Data clustering identifies the groups present in a data set, each of which contains related data instances. Network clustering identifies the groups present in a computer network, each of which contains highly connected computers. Network clustering returns the various topological structures present in a computer network as shown, whereas data clustering returns cluster sets of related data instances. The quality of data clustering is measured using metrics like intra-cluster distances (ICD), inter-cluster distances, F-measure, and accuracy. The quality of network clustering is measured using metrics like the global clustering coefficient and the average of the local clustering coefficients
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