Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of distance-based models for ranking data
Computational Statistics & Data Analysis
Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Aggregating inconsistent information: ranking and clustering
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Unsupervised rank aggregation with distance-based models
Proceedings of the 25th international conference on Machine learning
Unsupervised rank aggregation with domain-specific expertise
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Distance-based tree models for ranking data
Computational Statistics & Data Analysis
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
A grouped ranking model for item preference parameter
Neural Computation
An Exponential Model for Infinite Rankings
The Journal of Machine Learning Research
Clustering Algorithms for Chains
The Journal of Machine Learning Research
Model-based clustering of inhomogeneous paired comparison data
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Mixtures of weighted distance-based models for ranking data with applications in political studies
Computational Statistics & Data Analysis
Mode seeking over permutations for rapid geometric model fitting
Pattern Recognition
Riffled independence for efficient inference with partial rankings
Journal of Artificial Intelligence Research
Efficient vote elicitation under candidate uncertainty
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of filled rank positions cause heterogeneity in the data. We propose a mixture approach for clustering of heterogeneous rank data. Rankings of different lengths can be described and compared by means of a single probabilistic model. A maximum entropy approach avoids hidden assumptions about missing rank positions. Parameter estimators and an efficient EM algorithm for unsupervised inference are derived for the ranking mixture model. Experiments on both synthetic data and real-world data demonstrate significantly improved parameter estimates on heterogeneous data when the incomplete rankings are included in the inference process.