抽象的な

An Effective Way to Ensemble the Clusters

R.Saranya, Vincila.A, Anila Glory.H

Data Mining is the process of extracting knowledge hidden from huge volumes of raw data. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. In the context of extracting the large data set, most widely used partitioning methods are singleview partitioning and multiview partitioning. Multiview partitioning has become a major problem for knowledge discovery in heterogeneous environments. This framework consists of two algorithms: multiview clustering is purely based on optimization integration of the Hilbert Schmidt - norm objective function and that based on matrix integration in the Hilbert Schmidt - norm objective function . The final partition obtained by each clustering algorithm is unique. With our tensor formulation, both heterogeneous and homogeneous information can be integrated to facilitate the clustering task. Spectral clustering analysis is expected to yield robust and novel partition results by exploiting the complementary information in different views. It is easy to see to generalize the Frobenius norm on matrices. Instead of using only one kind of information which might contain the incomplete information, it extends to carry out outliers detection with multi-view data. Experimental results show that the proposed approach is very effective in integrating higher order of data in different settings