Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model

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1. Introduction

Clustering is a popular data analysis method and plays an important role in data mining. Clustering is an important class of unsupervised learning techniques. How to get the best or a satisfying solution quickly has great significance for improving productivity and the development of society. So far, it has been widely applied in many fields, like web mining, pattern recognition, resources allocation, machinelearning, spatial database analysis, artificial intelligence, and so on. The existing clustering algorithms can be simply classified into the following two categories: hierarchical clustering and partitional clustering [1]. Though the K-means algorithm is widely used to solve problems in many areas, KM is very sensitive to initialization, the better centers we choose, the better results we get. Also, it was easily trapped in local optimal [2]. Recently much work was done to overcome these problems. Traditional cluster analysis requires every point of the data set to be assigned into a cluster precisely, so we call it hard clustering. But in fact, most things exist in ambiguity in the attribute, there are no explicit boundaries among the things, and no the nature of either-or. So, the theory of the fuzzy clustering is more suitable for the nature of things, and it can more objectivly reflect the reality. To date, development of clustering has focused on data processing speed and efficiency. How appropriate the number of clusters may affect the clustering results. For example, a smaller number of clusters can help clearly identify the original data structure but key hidden information may not be obtained.

Data clustering was an exploratory or descriptive process for data analysis, where similar objects or data points were identified and grouped into a set...