a study of the effects of bias in criterion functions for temporal data clustering
In this paper, we study the bias associated with modeling methods and criterion functions used in temporal data clustering. In particular, we experimentally study two approaches on clustering discrete valued uni-variate temporal data. The first approach uses Markov chain models to capture the temporal relations encoded in data. The similarity between two sequences is computed as the average sequence to model likelihood. The second approach is distance based where Levenshtein string edit distance is applied to compute the edit distance between two sequences. Experiments are performed using these two approaches on web user data and on CS student online lab performance data. The characteristics of clustering results obtained from the two approaches are analyzed and recommendation about the suitable application for each approach is given.
temporal data clustering similarity measures markov chain clustering levenshtein distance clustering analysis clustering applications