期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2014
卷号：6
期号：05
页码：187-193
出版社：Engg Journals Publications
摘要：Due to the tremendous growth of data and large databases, efficient extraction of required data has become a challenging task. This paper propose a novel approach for knowledge discovery from huge unlabeled temporal databases by employing a combination of HMM and K-means technique. We propose to recursively divide the entire database into clusters having similar characteristics, this process is repeated until we get the cluster�s where no further diversification is possible. Thereafter, the clusters are labeled for knowledge extraction for various purposes.
