alexa Abstract | An Effective Way to Ensemble the Clusters
ISSN ONLINE(2319-8753)PRINT(2347-6710)

International Journal of Innovative Research in Science, Engineering and Technology
Open Access

Like us on:
OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Research Article Open Access


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

To read the full article Peer-reviewed Article PDF image | Peer-reviewed Full Article image

Author(s): R.Saranya, Vincila.A, Anila Glory.H

Share This Page

Additional Info

Loading Please wait..
Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version