Name
Title | ||
|---|---|---|
Temporal Pattern Generation Using Hidden Markov Model Based Unsupervised Classification |
Abstract | ||
|---|---|---|
This paper describes a clustering methodology for temporal data using hidden Markov model(HMM) representation. The proposed method improves upon existing HMM based clustering methods in two ways: (i) it enables HMMs to dynamically change its model structure to obtain a better fit model for data during clustering process, and (ii) it provides objective criterion function to automatically select the clustering partition. The algorithm is presented in terms of four nested levels of searches: (i) the search for the number of clusters in a partition, (ii) the search for the structure for a fixed sized partition, (iii) the search for the HMM structure for each cluster, and (iv) the search for the parameter values for each HMM. Preliminary experiments with artificially generated data demonstrate the effectiveness of the proposed methodology. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1007/3-540-48412-4_21 | IDA |
Keywords | Field | DocType |
clustering process,clustering partition,model structure,hidden markov model,fit model,clustering methodology,hmm structure,proposed methodology,temporal pattern generation,temporal data,unsupervised classification | Data mining,Signal processing,Bayesian information criterion,Computer science,Temporal database,Artificial intelligence,Cluster analysis,Pattern recognition,Marginal likelihood,Finite-state machine,Hidden Markov model,Partition (number theory),Machine learning | Conference |
Volume | ISSN | ISBN |
1642 | 0302-9743 | 3-540-66332-0 |
Citations | PageRank | References |
22 | 1.63 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cen Li | 1 | 37 | 3.51 |
| Gautam Biswas | 2 | 1594 | 233.43 |