《計算機應用研究》|Application Research of Computers

采用類心密度策略的多目標微分自動聚類算法

Multi-objective differential evolution automatic clustering algorithm based on class-center density

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作者 申曉寧,孫毅,薛云勇,孫帥
機構 南京信息工程大學 自動化學院,a.江蘇省天氣環境與裝備技術協同創新中心;b.江蘇省大數據分析技術重點實驗室,南京 210044
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文章編號 1001-3695(2019)11-004-3224-06
DOI 10.19734/j.issn.1001-3695.2018.04.0288
摘要 針對聚類過程中,由于類心選取的隨機性導致所選類心偏離數據集,或者類心過于集中而帶來的錯誤聚類這一缺陷,提出一種算法對類心的選取進行兩次篩選,即將類心密度過小的以及兩兩類心之間距離過小的類心分別篩選出來,不讓其參與聚類,此后算法對篩選后剩余的類心再進行聚類。為了使算法能較快地得到最優類心,提出了改進的聚類準則函數,對聚類數目進行動態懲罰。為了評估所提算法在聚類問題上的應用性能,選擇兩種不同類型的數據集進行了仿真實驗。與其他三種現有的自動聚類算法的比較結果表明,所提算法能夠獲得更好的聚類結果,從而驗證了算法所提策略的有效性。
關鍵詞 自動聚類; 類心密度策略; 類心篩選; 多目標優化; 微分進化
基金項目 國家自然科學基金資助項目(61502239)
江蘇省自然科學基金資助項目(BK20150924)
“江蘇省青藍工程”資助項目
本文URL http://www.ziusle.tw/article/01-2019-11-004.html
英文標題 Multi-objective differential evolution automatic clustering algorithm based on class-center density
作者英文名 Shen Xiaoning, Sun Yi, Xue Yunyong, Sun Shuai
機構英文名 a.CICAEET,b.B-DAT,School of Automation,Nanjing University of Information Science & Technology,Nanjing 210044,China
英文摘要 In the process of clustering, for the reason that the randomness of the class-center selection may lead to the phenomenon that the selected class-center deviates from the data set, or the class-center is too centralized, the proposed algorithm selected the class-center for two times: it screened out the class-centers which have too small density or have small distances between pairs of class-centers, and the algorithm did not allow them to participate in clustering. Then the algorithm continued to cluster the remaining class-centers. In order to make the algorithm get the optimal class-center quickly, it proposed an improved clustering criterion function to penalize the number of clusters dynamically. In order to evaluate the performance of the proposed algorithm on clustering problems, it carried out experiments on two types of data sets. Compared with the other three existing automatic clustering algorithms, simulation experiments show that the proposed algorithm can obtain better clustering results, which validates the effectiveness of the proposed strategies.
英文關鍵詞 automatic clustering; class-center density strategy; class-center screening; multi-objective optimization; diffe-rential evolution
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收稿日期 2018/4/20
修回日期 2018/6/21
頁碼 3224-3229
中圖分類號 TP301.6
文獻標志碼 A
中超外援名额