计算机应用与软件2018，Vol.35Issue(3)：230-235,252,7.DOI:10.3969/j.issn.1000-386x.2018.03.044
基于位置信息熵的局部敏感哈希聚类方法
LOCAL SENSITIVE HASH CLUSTERING METHOD BASED ON LOCATION INFORMATION ENTROPY
摘要
Abstract
In the analysis of massive biological sequences , the existing clustering algorithms have the problems of lowtime efficiency, low accuracy and insufficient biological significance of the clustering results .To solve these problems, alocal sensitive hash clustering method based on location information entropy was proposed .By using K words to calculatethe standard entropy of a biological sequence , the standard entropy was used as the eigenvector of the local sensitive hashfunction cluster, and the feature matrix was calculated and applied to the biological sequence clustering .Experimentalresults showed that the proposed algorithm effectively improved the efficiency of time and the accuracy of clustering .Asthe data set increased, the algorithm also achieved good results.The experimental results were more biologicallyinterpretative and practical.关键词
位置信息/标准熵/局部敏感哈希/生物序列聚类/编辑距离Key words
Location information/Standard entropy/Local sensitive hash/Biological sequence clustering/Edit dis-tance分类
信息技术与安全科学引用本文复制引用
徐彭娜,魏静,林劼,江育娥..基于位置信息熵的局部敏感哈希聚类方法[J].计算机应用与软件,2018,35(3):230-235,252,7.基金项目
国家自然科学基金项目(61472082) （61472082）
福建省自然科学基金项目(2014J01220). （2014J01220）
