8/13/2019 Titles With Abstract

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MINING ORDER-PRESERVING SUBMATRICES FROM DATA WITH REPEATED

MEASUREMENTS

Order-preserving submatrices (OPSMs) have been shown useful in capturing concurrent

patterns in data when the relative magnitudes of data items are more important than their

absolute values. To cope with data noise, repeated experiments are often conducted to collect

multiple measurements. We propose and study a more robust version of OPSM, where each data

item is represented by a set of values obtained from replicated experiments. We call the new

problem OPSM-RM (OPSM with repeated measurements). We define OPSM-RM based on a

number of practical requirements. We discuss the computational challenges of OPSM-RM and

propose a generic mining algorithm. We further propose a series of techniques to speed up twotimedominating components of the algorithm. We clearly show the effectiveness of our methods

through a series of experiments conducted on real microarray data.