Clustering Algorithm In Data Mining Research

With the development of information science and technology, the database management system have been applied more and more widely, and the size of the database has continued to expand, people have accumulated massive amount of business data, and how to find the valuable information in the vast ocean-like data have become an urgent need to be solved. From this data mining techniques have emerged,which is one of the most cutting-edge research of the database and information decision-making. Cluster analysis as an important branch of data mining is the analysis of data's similarity, and divided the large data sets into groups, in which the data inside the same group was most similar to each other and the data in different groups was differ from each other. Clustering is an effective means of finding useful information.Based on the above study, this paper mainly discusses C-means clustering method which based on the immune genetic algorithm and particle swarm optimization algorithm separately. Following is the main work has been done:1.Using density clustering algorithm fast convergence, hierarchical clustering algorithms can be at different levels of data granularity to detect, and very easy to implement similarity measure or distance metric advantages to acquire a new density-based hierarchical clustering algorithm, the hierarchical clustering algorithm to overcome the time complexity of the problem, get a better clustering results2.The immune algorithm fuzzy clustering algorithm, fuzzy clustering algorithm to overcome the initial value sensitive easily trapped into local optimization problem. And the new clustering algorithm can not cluster the given initial conditions, the number of accurate clustering results3.Combining the traditional clustering algorithms and fuzzy clustering algorithm.Focal point for the use of density algorithm is not sensitive to the advantages of the density of fuzzy clustering algorithm is applied to obtain a new clustering algorithm is applied to large data sets the amount of data, its accuracy rate was significantly higher than the fuzzy clustering algorithm and immune algorithm.