Algorithms for clustering data
Algorithms for clustering data
Local discriminant bases and their applications
Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
The String-to-String Correction Problem
Journal of the ACM (JACM)
ACM Computing Surveys (CSUR)
Bayesian Clustering by Dynamics
Machine Learning - Special issue: Unsupervised learning
Pattern discovery in sequences under a Markov assumption
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining tasks and methods: Clustering: conceptual clustering
Handbook of data mining and knowledge discovery
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Model-Based Clustering and Visualization of Navigation Patterns on a Web Site
Data Mining and Knowledge Discovery
Supervised classification with temporal data
Supervised classification with temporal data
A bayesian approach to temporal data clustering using the hidden markov model methodology
A bayesian approach to temporal data clustering using the hidden markov model methodology
Adaptive teaching strategy for online learning
Proceedings of the 10th international conference on Intelligent user interfaces
Modeling student online learning using clustering
Proceedings of the 44th annual Southeast regional conference
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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.