A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Editorial: recent developments in mixture models
Computational Statistics & Data Analysis
Cluster analysis of heterogeneous rank data
Proceedings of the 24th international conference on Machine learning
A mixture model for preferences data analysis
Computational Statistics & Data Analysis
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
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
Vote elicitation with probabilistic preference models: empirical estimation and cost tradeoffs
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Mixtures of weighted distance-based models for ranking data with applications in political studies
Computational Statistics & Data Analysis
A generative model for rank data based on insertion sort algorithm
Computational Statistics & Data Analysis
Elicitation and approximately stable matching with partial preferences
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Efficient vote elicitation under candidate uncertainty
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Ranking data arises when judges are asked to rank some or all of a group of objects. Examples of ranking data arise in many areas, including the Irish electoral system and the Irish college admission system. Mixture models can be used to study heterogeneous populations. The study of these populations is achieved by thinking of the population as being composed of a finite number of homogeneous sub-populations. Mixtures of distance-based models are used to analyze ranking data from heterogeneous populations. Results from simulations are included, as well as an application to the well-known American Psychological Association election data set.