一种基于信息熵的空间聚类算法

Space Clustering Algorithm Based on Information Entropy

空间数据挖掘技术是从空间数据库中提取隐含的、用户感兴趣的知识.针对当前的聚类算法没有很好考虑到空间数据的复杂性和数据之间的联系,再加上聚类的精确度不高,设计了一种新的算法—基于信息熵的空间聚类算法(ESCA算法),该算法优先考虑空间数据的复杂性和数据之间的联系,并采用蚁群优化机制改善传统算法中聚类簇数不确定的缺点.实验结果表明该算法是可行,并且具有更高的精确度.

Spatial data mining is to extract implicit spatial database,users are interested in knowledge.In this paper,the current clustering algorithm is not well taken into account the complexity of spatial data and the data link between the accuracy of clustering together is not high,designed a new algorithm-the space together based on information entropy Class of algorithm（ESCA algorithm）,the algorithm complexity of spatial data priority and data link between the mechanisms by using ant colony optimization algorithm to improve the traditional shortcomings of cluster cluster number of uncertainties.Experimental results show that the algorithm is feasible and has higher accuracy.