Statistical methods for speech recognition
Statistical methods for speech recognition
Algorithms for Model-Based Gaussian Hierarchical Clustering
SIAM Journal on Scientific Computing
An introduction to model selection
Journal of Mathematical Psychology
Clustering based on conditional distributions in an auxiliary space
Neural Computation
A Bayesian Approach to Temporal Data Clustering using Hidden Markov Models
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Hidden Markov Model-Based Approach to Sequential Data Clustering
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A unified framework for model-based clustering
The Journal of Machine Learning Research
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGMOD Record
FACT: A New Fuzzy Adaptive Clustering Technique
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
Clustering Distributed Time Series in Sensor Networks
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Fuzzy ensemble clustering based on random projections for DNA microarray data analysis
Artificial Intelligence in Medicine
Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Mixture-model cluster analysis using information theoretical criteria
Intelligent Data Analysis
Clustering of time series data-a survey
Pattern Recognition
A review on time series data mining
Engineering Applications of Artificial Intelligence
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
Temporal Data Clustering via Weighted Clustering Ensemble with Different Representations
IEEE Transactions on Knowledge and Data Engineering
Piecewise cloud approximation for time series mining
Knowledge-Based Systems
A novel clustering method on time series data
Expert Systems with Applications: An International Journal
Bagging-based spectral clustering ensemble selection
Pattern Recognition Letters
Minimum encoding approaches for predictive modeling
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Time Series Clustering Via RPCL Network Ensemble With Different Representations
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Model-based clustering of high-dimensional data: A review
Computational Statistics & Data Analysis
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Temporal data have many distinct characteristics, including high dimensionality, complex time dependency, and large volume, all of which make the temporal data clustering more challenging than conventional static datasets. In this paper, we propose a HMM-based partitioning ensemble based on hierarchical clustering refinement to solve the problems of initialization and model selection for temporal data clustering. Our approach results four major benefits, which can be highlighted as: (i) the model initialization problem is solved by associating the ensemble technique; (ii) the appropriate cluster number can be automatically determined by applying proposed consensus function on the multiple partitions obtained from the target dataset during clustering ensemble phase; (iii) no parameter re-estimation is required for the new merged pair of cluster, which significantly reduces the computing cost of its final refinement process based on HMM agglomerative clustering; and finally (iv) the composite model is better in characterizing the complex structure of clusters. Our approach has been evaluated on synthetic data and time series benchmark, and yields promising results for clustering tasks.