Research And Application Of K-means Clustering Algorithm

Clustering is a major field in data mining which also is an important method of data partition or grouping. Clustering now has been applied into various ways in commerce, market analysis, biology, Web classification and so on. And clustering algorithms includes partitioning, hierarchical, density-based, grid-based, model-based algorithm and fuzzy clustering.K-means algorithm is one of the essential clustering algorithms. It is a kind of clustering algorithm based on partitioning method. The thesis is planned to improve the algorithm based on the research, while on the application aspect, the thesis take the algorithm into the customer segmentation use. Customer segmentation is the essential element for the enterprise to take out CRM.In the first part of the paper, it shows the main elaboration object of study background material as well as the goal this article must achieve, and shows the research the mentality and the overall content.The second part mainly introduces the basic knowledge of clustering and methods for clustering analysis, on the basis of the different algorithms analysis, getting the advantages and disadvantages through compare of different algorithms.The third part introduce the application of the algorithm, the thesis applies the clustering technology in the use of the customer segmentation, First building the customer value system through AHP; quantify customer value; then divide customer into different classifications using clustering technology, so can take out the efficient CRM according to the deferent customer classification. Currently there are some evaluating systems of customer value, but none of them can be put into practice efficiently, The thesis built an evaluating system of customer value which is in line with the development of the enterprise, using the method of data mining, based on the practical situation of the enterprise and through series of practical evaluating index of customer value, The evaluating system can be used to quantify customer value, segment customer and build decision supporting system of the customer value management,The fourth part is the core part, mainly analysis the typical algorithm-K-means, the thesis proposed two algorithms to improve the k-means algorithm. Improved algorithms A can get the K automatically, and can ensure achieve the global optimum value to some degree. Improved algorithm B which combined the sample technology and arrangement agglomeration algorithm is much efficient than the k-means algorithm.At the end, recount the main job of the thesis, and point the further research direction,...