Publication Type : Conference Paper
Publisher : Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014
Source : Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014, Springer International Publishing, Volume 1, Cham, p.201–209 (2014)
Url : https://link.springer.com/chapter/10.1007%2F978-3-319-11933-5_23
ISBN : 9783319119335
Keywords : attribute relevance index, Attribute selection, LASSO, penalized regression, Projected clustering, sparsity problem
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Computing
Department : Computer Science
Year : 2014
Abstract : It has always been a major challenge to cluster high dimensional data considering the inherent sparsity of data-points. Our model uses attribute selection and handles the sparse structure of the data effectively. We select the most informative attributes that do preserve cluster structure using LASSO (Least Absolute Selection and Shrinkage Operator). Though there are other methods for attribute selection, LASSO has distinctive properties that it selects the most correlated set of attributes of the data. This model also identifies dominant attributes of each cluster which retain their predictive power as well. The quality of the projected clusters formed, is also assured with the use of LASSO.
Cite this Research Publication : Lidiya Narayanan, Anoop S. Babu, and Dr. M. R. Kaimal, “Projected Clustering with LASSO for High Dimensional Data Analysis”, in Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014, Cham, 2014, vol. 1, pp. 201–209.