Hunting for Coherent Co-clusters in High Dimensional and Noisy Datasets

Clustering problems often involve datasets where only a part of the data is relevant to the problem, e.g., in microarray data analysis only a subset of the genes show cohesive expressions within a subset of the conditions/features. The existence of a large number of non-informative data points and features makes it challenging to hunt for coherent and meaningful clusters from such datasets. Additionally, since clusters could exist in different subspaces of the feature space, a co-clustering algorithm that simultaneously clusters objects and features is often more suitable as compared to one that is restricted to traditional “one-sided” clustering. We propose Robust Overlapping Co-clustering (ROCC), a scalable and very versatile framework that addresses the problem of efficiently mining dense, arbitrarily positioned, possibly overlapping co-clusters from large, noisy datasets. ROCC has several desirable properties that make it extremely well suited to a number of real life applica...

• Type: Conference
• Year: 2008
• Where: ICDM
• Authors: Meghana Deodhar, Joydeep Ghosh, Gunjan Gupta, Hyuk Cho, Inderjit S. Dhillon
• Keywords: Data Mining, ICDM 2008, Microarray Data, Microarray Data Analysis, Non-informative Data Points