Data Mining Clustering Algorithm And System Design

Data mining is a new technique, which has become increasingly popular in recent years. People can discover valuable rules behind the data that can support the science decision. Now data mining has become a subject, which involves lots of science domain and technology such as database, pattern recognition, neural network and computational intelligence etc.Firstly, this dissertation introduces the basic concepts, tasks, functions, applications and development way of data mining. We make a brief summary about the clustering algorithms of data mining, and make an detail study about the clustering algorithms for high-dimension and complex data.Secondly, a new algorithm based on the idea of coverage density is proposed for clustering categorical data. It uses average coverage density as the global criterion function. Large sparse categorical databases can be clustered effectively by using this algorithm. It shows that the algorithm uses less memory and time by analyzing its time and space complexity. Experiments on two real datasets are carried out to illustrate the performance of the proposed algorithm.Then, we propose a new self-growing hierarchical principal components analysis self-organizing neural networks model for high-dimension and complex data. This dynamically growing model expands the ability of the PCASOM model that represents the hierarchical structure of the input data. It overcomes the shortcoming of the PCASOM model in which the fixed the network architecture must be defined prior to training. Experiment results showed that the proposed model has better performance in the tradition clustering problem.And then, a data mining service system based web is presented and implemented for research of data mining and application of data mining. The author has put due emphasis on the architecture design, function design, component design and implementation technologies of the system .