期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2014
卷号：3
期号：3
页码：10365
出版社：S&S Publications
摘要：Data Mining is the process of extracting knowledge hidden from huge volumes of raw data. The overall goalof the data mining process is to extract information from a data set and transform it into an understandable structure forfurther use. In the context of extracting the large data set, most widely used partitioning methods are singleviewpartitioning and multiview partitioning. Multiview partitioning has become a major problem for knowledge discoveryin heterogeneous environments. This framework consists of two algorithms: multiview clustering is purely based onoptimization integration of the Hilbert Schmidt - norm objective function and that based on matrix integration in theHilbert Schmidt - norm objective function . The final partition obtained by each clustering algorithm is unique. Withour tensor formulation, both heterogeneous and homogeneous information can be integrated to facilitate the clusteringtask. Spectral clustering analysis is expected to yield robust and novel partition results by exploiting the complementaryinformation in different views. It is easy to see to generalize the Frobenius norm on matrices. Instead of using only onekind of information which might contain the incomplete information, it extends to carry out outliers detection withmulti-view data. Experimental results show that the proposed approach is very effective in integrating higher order ofdata in different settings.
