改进的CSFCM聚类算法及其在赤潮监测中的应用  

Application of improved CSFCM clustering in red tide monitoring

作　　者：王兴强[1] 刘长兴[1] 刘国伟[1] 张旭东[2] 

机构地区：[1]济南军区总医院信息科 [2]中国人民解放军72515部队

出　　处：《计算机工程与应用》2012年第8期233-235,245,共4页Computer Engineering and Applications

基　　金：山东省科技攻关计划(No.2004GG2205108)

摘　　要：为满足海洋监测系统中赤潮监测数据的信息挖掘需求,在深入探讨数据挖掘相关算法的基础上,提出一种新的基于分组属性加权聚类的CSFCM算法。该算法将COSA(Clustering Objects on Subsets of Attributes)算法与模糊C均值算法相结合并引入相似关系预处理,再对准则函数和聚类模型加以改进。实验结果表明,该算法适用于赤潮监测数据挖掘的实时聚类需求,准确率高,为赤潮预报提供必要的决策依据。In order to satisfy data mining requirements of red tide monitoring data in marine observation system, a novel CSFCM clus- tering with subsets of attribute＇ s weights, based on thorough discussion of algorithms for data mining, is introduced. In the proposed method, a classic COSA（Clustering Object on Subsets of Attributes） algorithm and the fuzzy C-means algorithm are adopted with pre- treatment of similary relation and then the criterion function and clustering model are improved. The experimental results demonstrate the applicability, feasibility and efficiency of this new algorithm on data mining of red tide monitoring, and it provides necessary decisionmaking basis for red tide forecast.

关 键 词：赤潮监测 属性子集的聚类对象(COSA) 聚类 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]