Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
ACM Computing Surveys (CSUR)
Clustering by soft-constraint affinity propagation
Bioinformatics
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
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Clustering is a classical unsupervised learning technique which has wide applications. One popular clustering model seeks a set of centers and organizes the data into different groups, with an objective to maximize the net similarities within each cluster. In this paper, we first formulate a generalized form of the clustering model, where the similarity measure has uncertainties or changes in different states. Then we propose an affinity propagation-based algorithm, which gives an efficient and accurate solution to the generalized model. Finally we evaluate the model and the algorithm by experiments. The results have justified the usefulness of the model and demonstrate the improvements of the algorithm over other possible solutions.