| Facing the massive volume and high dimensional data, it is one of research points of data mining on how to build effective and expandable clustering algorithm for data mining.Aiming at above issues, the author substantially studied clustering algorithms as follows:Analyse the advantages and disadvantages of the present initializations and propose a method based on the distance optimization,which does not need a threshold, is not affected from the order of data set, is insensitive to outliers or noise, and is available to the clustering of a very large data set.According to the disadvantages of the clustering algorithm based density and gird, a clustering algorithm (CUBN) is presented, which integrates density-based, gird-based and distance-based clustering methods.The method can identify clusters having non-spherical shapes, size and different denisty and can effectively filter noise data, with simple parameters, It has near linear-time complexity and is available to the clustering of a very large data set.According to the existing shortcomings of hierarchy clustering,a clustering algorithm(CMM) is presented, which used the division and classified the data clusters of atom and clustered hierarachy and finally came to the results.It is more robust to outliers, and wide variances in size.with the implementation of the advantages of high efficiency.According to the existing problems in the teaching evaluation system of university, on the study of clustering algorithm, the article conducted the prototype study based on the data mining teaching evaluation system,introduced Analytic Hierarehy Proeess of the scientific of decision-making and resolved the index of value creation and distribution;on the study of the association rulers based on the algorithm, the article took the study of clustering analysis to college evaluation data, reduced effectively the volumn of data analysis and overcame the unreasonability of classification. |
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