Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Dynamic syslog mining for network failure monitoring
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Online Model Selection Based on the Variational Bayes
Neural Computation
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A linear-time algorithm for computing the multinomial stochastic complexity
Information Processing Letters
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Online clustering of high-dimensional trajectories under concept drift
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
The performance of universal encoding
IEEE Transactions on Information Theory
Dynamic Model Selection With its Applications to Novelty Detection
IEEE Transactions on Information Theory
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We are concerned with the issue of detecting changes of clustering structures from multivariate time series. From the viewpoint of the minimum description length(MDL) principle, we propose an algorithm that tracks changes of clustering structures so that the sum of the code-length for data and that for clustering changes is minimum. Here we employ a Gaussian mixture model(GMM) as representation of clustering, and compute the code-length for data sequences using the normalized maximum likelihood (NML) coding. The proposed algorithm enables us to deal with clustering dynamics including merging, splitting, emergence, disappearance of clusters from a unifying view of the MDL principle. We empirically demonstrate using artificial data sets that our proposed method is able to detect cluster changes significantly more accurately than an existing statistical-test based method and AIC/BIC-based methods. We further use real customers' transaction data sets to demonstrate the validity of our algorithm in market analysis. We show that it is able to detect changes of customer groups, which correspond to changes of real market environments.