毕业设计-计算机研究生毕业论文基于遗传算法的k-means聚类挖掘算法的研究.doc

算法的聚类结果进行比较,实验结果表明本文算法能够有效地解决聚类问题｡
关键词:数据挖掘聚类分析遗传算法 k-means算法改进的遗传k-means算法
RESEARCH OF K-MEANS CLUSTERING IN DATA MINING BASED ON IC ALGORITHM
ABSTRACT
Data mining is a new subject formed with the development of the information technology and is a new research point in the information and database technology. The purpose of data mining is to discovery hidden and useful knowledge from huge amounts of data, which can support the science decision.
Cluster analysis is one of the important themes in data mining. Clustering is a unsupervised classifying method, the goal of clustering is to partition data set into such clusters that objects within a cluster have high similarity parison to one another, but are very dissimilar to objects in other clusters without any prior knowledge. As a classical method of clustering analysis, k-means has been widely used merce, market analysis, biology, text classification and so on. However k-means has two severe defects—sensitive to initial data and easy to get into a local optimum. On this condition, improving k-means is an effective method to get better clustering result.
Firstly, the dissertation detailedly introduce clustering analysis technology, and most existing clustering algorithms are classified, analysis their advantages and disadvantages. On the basis, the dissertation chooses k-means as research target.
Secondly, analyzing an important method—ic algorithms in data mining. On this basis, a new clustering method of k-means based on improved ic algorithm is proposed. The dissertation discussers and analyses the new algorithms in detail from coding method, fitness function, selection operators, crossover operators, mutation operators, k-means operators and other aspects.
Finally, for testing the performance of the proposed algorithms, the dissertation gives three simulation experiments. Simulation results show paring with k-means method, the proposed can get a better clustering result.
KEY WORDS:Data mining Cluster an