Spatial-Temporal Data Mining Procedure: LASR

Xiao-Feng Wang, Cleveland Clinic Lerner Research Institute
Jiayang Sun, Case Western Reserve University
Kath Bogie, Case Western Reserve University

Abstract

This paper is concerned with the statistical development of our spatial-temporal data mining procedure, LASR (pronounced "laser"). LASR is the abbreviation for Longitudinal Analysis with Self-Registration of largep-small-n data. It was motivated by a study of "Neuromuscular Electrical Stimulation" experiments, where the data are noisy and heterogeneous, might not align from one session to another, and involve a large number of multiple comparisons. The three main components of LASR are: (1) data segmentation for separating heterogeneous data and for distinguishing outliers, (2) automatic approaches for spatial and temporal data registration, and (3) statistical smoothing mapping for identifying "activated" regions based on false-discovery-rate controlled p-maps and movies. Each of the components is of interest in its own right. As a statistical ensemble, the idea of LASR is applicable to other types of spatial-temporal data sets beyond those from the NMES experiments.