Novel Fuzzy Clustering Algorithm Based On Nature Inspired Computation

In recent years, data mining is becoming one of the most advanced and active research topics in the field of the information decision-making in the world. As an effective tool of data mining, cluster analysis is attracting wide attention. Clustering is one of methods of multivariant statistical analysis, and one of important branches of unsupervised classification in statistical pattern recognition. In the recent two decades, cluster analysis is made rapid development, and many new algorithms have been proposed for various applications.Nature-inspired computation is a novel computing method, which is modeled based on the functions, characteristics and mechanism of organism in nature. It has adaptive, self-organized and self-learning abilities, which can be used to solve many complex problems. So, nature-inspired computation is becoming research hotspot, and has been widely used in some applications.To overcome the faultiness, even serious drawbacks of the available clustering algorithms for the applications in data mining, by combining the nature-inspired computation, the traditional cluster analysis algorithms are systematically improved and innovated in this thesis. The emphasis is put on the definition and optimization method of the objective function of the clustering algorithm for mixed attributes data set. And a new clustering algorithm with network structure is proposed for large-scale data sets, which extends the application ranges of cluster analysis. In addition, a novel cluster validity function is developed for data mining. The experimental results illustrate the effectiveness and good performance of the proposed new ideas and new methods on fuzzy cluster analysis.In sum, the main research fruits achieved in this thesis are given as follows.With the definition of a new matching dissimilarity measure, an objective function of clustering algorithm is developed by modifying the common cost function, i.e., the trace of the within cluster dispersion matrix, which is suitable for analysis of data sets with mixed attributes. The genetic algorithms (GA) is employed to optimize the developed objective function for overcoming the drawback of the traditional fuzzy k-means (FKM) algorithm, i.e., sensitivity to the initialization. The GA-based clustering algorithm can converge to the global optima with a high probability.To avoid the premature phenomenon of the GA algorithm, the clonal selectionalgorithm (CSA) in artificial immune system (AIS) is employed to optimize the developed objective function of cluster analysis. Since the CSA belongs to the population search strategy with intrinsic parallelism and stochastic search mechanism, it can converge to the global optima with probability of 1 and with higher speed over the GA-based algorithm. So, the CSA-based clustering algorithm is suitable for the large-scale data sets.By combining the artificial immune network (AIN) theory in AIS, an AIN-based clustering algorithm is presented for automatically obtaining the number and type of the cluster prototypes. Since the new algorithm can detect some neurons as the typical samples, the obtained neurons can be used to partition the data set into subsets. Then by analyzing the connected weights among the neurons with the minimal spanning tree (MST), the final clustering result will be achieved automatically with proper cluster number and prototypes.Following one of the immune adjustment methods in biology immune system, i.e., forbidden clone, a new clonal operator, forbidden clone is proposed. By combining the forbidden clone and the CSA, a clonal-operator-based clustering algorithm with network structure is formed. Since the new algorithm has both the characteristics of immune patience and the immune speciality, it can construct an effective clear network structure, which makes the clustering algorithm insensitive to the boundary points and the noise points.Imitating the resource-limited theory in immune system, a novel clustering method with fuzzy network structure based on limited resource is developed to realize the automat...