Bayesian Possibilistic Clustering Method with Optimality Guarantees

LIU Jiefang; WANG Shitong; WANG Jun; DENG Zhaohong

doi:10.11999/JEIT160908

Volume 39 Issue 7

Jul. 2017

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LIU Jiefang, WANG Shitong, WANG Jun, DENG Zhaohong. Bayesian Possibilistic Clustering Method with Optimality Guarantees[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1554-1562. doi: 10.11999/JEIT160908

Citation:

LIU Jiefang, WANG Shitong, WANG Jun, DENG Zhaohong. Bayesian Possibilistic Clustering Method with Optimality Guarantees[J]. Journal of Electronics & Information Technology, 2017, 39(7): 1554-1562. doi: 10.11999/JEIT160908

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Bayesian Possibilistic Clustering Method with Optimality Guarantees

doi: 10.11999/JEIT160908

1.
2.
(School of Digital Media, Jiangnan University, Wuxi 214122, China)

Funds:

The National Natural Science Foundation of China (61572236), Jiangsu Province Outstanding Youth Fund (BK20140001), Natural Science Foundation of Jiangsu Province (BK20151299)

Received Date: 2016-09-09
Rev Recd Date: 2017-02-10
Publish Date: 2017-07-19

Abstract

Abstract

A novel Bayesian possibilistic clustering method with optimality guarantees based on probability theory and possibilistic theory is proposed. First, the unknown membership degree and cluster center are represented as random variables. Given the specific constraints and uncertainty associated with each random variable, an appropriate probability distribution for each random variable is selected and the Bayesian possibilistic clustering model is proposed. On this basis, a novel Bayesian possibilistic clustering method with the optimal guarantee properties is propsed based on Bayesian theory and Monte Carlo sampling method using a Maximum-A-Posteriori (MAP) framework. Then, the convergence of the algorithm and the complexity of the algorithm are discussed. Experimental results on synthetic and real data sets show that the proposed method extends the traditional possibilistic clustering performance, and improves the clustering results.
- Possibilistic clustering,
- Bayesian inference,
- Maximum-A-Posteriori (MAP),
- Monte Carlo method

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References(28)

References

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