Browse reports by year:
[ ALL 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 ]
Browse report authors:
[ ALL A B C D E F G H I J K L M N O P Q R S T U V W X Y Z ]
Browse reports by title:
[ ALL A B C D E F G H I J K L M N O P Q R S T U V W X Y Z ]

University of Minnesota - Computer Science and Engineering Technical Report Abstract

Two-Dimensional Association Analysis For Finding Constant Value Biclusters In Real-Valued Data

Report Number: 09-020
Date of Submission: 7/7/2009

Authors:
   
   
   
   
   

View Report:
   PDF format

Abstract:

Biclustering is a commonly used type of analysis for real-valued data sets, and several algorithms have been proposed for finding different types of biclusters. However, no systematic approach has been proposed for exhaustive enumerating all (nearly) constant value biclusters in such data sets, which is the problem addressed in this paper. Using a monotonic range measure to capture the coherence of values in a block/submatrix of an input data matrix, we propose a two-step Apriori-based algorithm for discovering all nearly constant value biclusters, referred to as Range Constrained Blocks (RCBs). By systematic evaluation on an extensive genetic interaction data set, we show that the submatrices with similar values represent groups of genes that are functionally related than the biclusters with diverse values. We also show that our approach can exhaustively find all the biclusters with a range less than a given threshold, while the other competing approaches can not find all such biclusters.