期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：67
期号：1
出版社：Journal of Theoretical and Applied
摘要：As the information flooding onto worldwide web is growing fast, it is very difficult to find the hidden knowledge from huge data stored. In traditional clustering techniques, the task for clustering algorithm is to find the relevant data, describing relationships among the elements between two input datasets. During unsupervised learning process, metadata may play a significant role in the context of data retrieval. In this work, we proposed a methodology of metadata based clustering model (MBCM) for large datasets. The proposed model is validated with few standard datasets like IEEE, ACM and Cluto etc. Experimental results show that it is possible to achieve better cluster quality without significant overhead in terms of execution time. Finally, the performance of our proposed model is evaluated using F-measure and the performance of our method is compared with existing clustering models.
关键词：Metadata; clustering; data mining; dataset; K medoids.
