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一種具有最優(yōu)保證特性的貝葉斯可能性聚類方法

劉解放,  王士同,  王駿,  鄧趙紅

劉解放, 王士同, 王駿, 鄧趙紅. 一種具有最優(yōu)保證特性的貝葉斯可能性聚類方法[J]. 電子與信息學(xué)報, 2017, 39(7): 1554-1562. doi: 10.11999/JEIT160908
引用本文: 劉解放, 王士同, 王駿, 鄧趙紅. 一種具有最優(yōu)保證特性的貝葉斯可能性聚類方法[J]. 電子與信息學(xué)報, 2017, 39(7): 1554-1562. doi: 10.11999/JEIT160908
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

一種具有最優(yōu)保證特性的貝葉斯可能性聚類方法

doi: 10.11999/JEIT160908
  • 2.

    (江南大學(xué)數(shù)字媒體學(xué)院 無錫 214122) ②(湖北交通職業(yè)技術(shù)學(xué)院交通信息學(xué)院 武漢 430079)

基金項目: 

國家自然科學(xué)基金(61572236)，江蘇省杰出青年基金(BK20140001)，江蘇省自然科學(xué)基金(BK20151299)

Bayesian Possibilistic Clustering Method with Optimality Guarantees

  • 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)

  • 摘要: 該文結(jié)合概率理論和可能性理論，提出一種具有最優(yōu)保證特性的貝葉斯可能性聚類新方法。首先，將未知隸屬度和聚類中心作為隨機變量，為每個隨機變量選擇一個合適的概率分布，提出貝葉斯可能性聚類模型；在此基礎(chǔ)上，基于貝葉斯推理和和蒙特卡洛采樣方法，通過最大后驗概率框架求解貝葉斯可能性聚類模型中的未知參數(shù)，從而提出一種具有最優(yōu)保證特性的貝葉斯可能性聚類新方法。并對算法收斂性、算法復(fù)雜度等方面作了理論探討。在合成數(shù)據(jù)集和真實數(shù)據(jù)集上的實驗表明，所提算法擴展了傳統(tǒng)可能性聚類性能，改進了聚類結(jié)果。
    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.
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出版歷程
  • 收稿日期:  2016-09-09
  • 修回日期:  2017-02-10
  • 刊出日期:  2017-07-19

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