Search

Soft subspace clustering are effective clustering techniques for high dimensional datasets. Although several soft subspace clustering algorithms have been developed in recently years, its robustness should be further improved. In this work, a novel soft subspace clustering algorithm RSSKM are proposed. It is based on the incorporation of the alternative distance metric into the framework of k-means type algorithm for soft subspace clustering and can automatically calculates the feature weights of each cluster in the clustering process. The properties of RSSKM are also investigated. Experiments on real world text datasets are conducted and the results show that RSSKM outperformed some popular clustering algorithms for text mining, while still maintaining efficiency of the k-means clustering process.

Clustering algorithm is very important for data mining. Fuzzy c-means clustering algorithm is one of the earliest goal-function clustering algorithms, which has achieved much attention. This paper analyzes the lack of fuzzy C-means (FCM) algorithm and genetic clustering algorithm. Propose a hybrid clustering algorithm based on immune single genetic and fuzzy C-means. This algorithm uses the fuzzy clustering of Immune Partheno-Genetic to guide the number and the choice of the clustering centers. And then utilize FCM to make the clustering (IPGA-FCM). This algorithm not only overcomes the local optimal problem of FCM, the choice of the initial value is inappropriate, but also overcomes the contradictions between the search speed and clustering accuracy of the general genetic clustering algorithm. Then it applies the novel clustering algorithm to Chinese document clustering. The clustering algorithm is superior to other ordinary clustering algorithm and the result can embody the wide diversity and large amount of Chinese document. Experiments show the algorithm is effective. Index Terms—Clustering analysis, genetic algorithm, FCM, Immune mechanism, Text clustering

To effectively organize and analyze massive web information, design a web user’s clustering mining algorithm. SOM neural network algorithm has lots of disadvantages, to solve the data clustering, propose a new method that uses D-SOM (Dissimilarity-Self Organizing feature Mapping) algorithm, for clustering web user’s. This algorithm can estimate the center and number of clustering data set by dissimilarity computing, optimize SOM neural network learning and improve clustering effect. Through design the experiment, these web data are collected and processed by D-SOM algorithm Experimental results verify which D-SOM clustering algorithm has better clustering accuracy and imore efficient than SOM neural network algorithm.