Discovery of Versatile Temporal Subspace Patterns in 3-D Datasets

Abstract

Most existing methods for clustering temporal data are based on either a strict similarity metric or a precisely defined temporal profile such as a sine, exponential wave etc. Also, these methods compute similarity metric across the entire time-span of the objects. However these types of temporal patterns are more useful in many biological analysis, where it is important to observe gene expression pattens across arbitrary subintervals. These types of temporal patterns are very useful in bioinformatics, where it is important to observe gene expression pattens across arbitrary subintervals. In this paper we present an algorithm for searching for multiple contiguous temporal subintervals within which the selected objects demonstrate existence of clear patterns. We demonstrate the power and advantages of our algorithm by using a synthetic dataset and a pharmacokinetics dataset for which other researchers have recently published their results. We compare and contrast our results with these results to show superiority of our approach.