Abstract
As an important data analysis method in data mining, clustering analysis has been researched extensively and in depth. Aiming at the limitation of K-means clustering algorithm that it is sensitive to the distribution of initial clustering center, Glowworm Swarm Optimization (GSO) Algorithm is introduced to solve clustering problems. Firstly, this paper introduces the basic ideas of GSO algorithm, K-means algorithm, and good-point set and analyzes the feasibility of combining them for clustering optimization. Next, it designs a clustering method of improved GSO algorithm based on good-point set which combines GSO algorithm and classical K-means algorithm together, searches data object space, and provides initial clustering centers for K-means algorithm by means of improved GSO algorithm and thus obtains better clustering results. Major improvement of GSO algorithm is to optimize the initial distribution of glowworm swarm by introducing the theory and method of good-point set. Finally, the new clustering algorithm is applied to UCI data sets of different categories and numbers for clustering test. The advantages of the improved clustering algorithm in terms of sum of squared errors (SSE), clustering accuracy, and robustness are explained through comparison and analysis.
Highlights
As an unsupervised data analysis method, clustering analysis is widely applied in such fields as data mining, pattern recognition, machine learning, and artificial intelligence [1]
This paper proposes an improved Glowworm Swarm Optimization (GSO) algorithm based on good-point set to solve clustering problems on the basis of analysis of relevant algorithms above and characteristics of clustering problems
K-means algorithm relies on initial clustering centers, which leads to large difference in the clustering result, low accuracy, and lack of stability of traditional K-means algorithm
Summary
As an unsupervised data analysis method, clustering analysis is widely applied in such fields as data mining, pattern recognition, machine learning, and artificial intelligence [1]. Hierarchy-based clustering methods mainly include CURE algorithm [4] and Chameleon algorithm [5], of which one cluster is represented by multiple points in CURE algorithm, making the processing of nonspherical data sets better. Representative algorithms of density-based clustering methods include DBSCAN algorithm [6], which is able to effectively identify class cluster of any shape, but is very sensitive to the setting of artificial parameters (e.g., radius). Rodriguez and Laio put forward a new density-based density peaks clustering (DPC) algorithm [7] in 2014 In this algorithm, density peaks (i.e., clustering centers) are selected manually through “decision diagram” first, and residual data points are allocated to each clustering center on this basis to obtain corresponding clustering result. A clustering analysis method combining PSO and K-means is proposed in literature [8] through the global
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