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

基于自然最近鄰相似圖的譜聚類

Spectral clustering based on natural nearest neighbor similarity graph

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作者 劉友超,張曦煌
機構 江南大學 物聯網工程學院,江蘇 無錫 214122
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文章編號 1001-3695(2020)01-006-0030-04
DOI 10.19734/j.issn.1001-3695.2018.06.0460
摘要 傳統譜聚類算法經常在處理一些結構復雜的數據集時效果不太理想,并且其相似度矩陣構造時參數的選取往往需要依靠多次實驗及個人經驗。在這種情況下,提出一種基于自然最近鄰相似圖的譜聚類(NSG-SC)算法。自然最近鄰是一種新穎的最近鄰概念,可以有效地避免K最近鄰以及ε-最近鄰方法需要人為設置參數的缺點。該算法構造相似度矩陣時依靠數據集自身的特性進行搜索,避免了參數選取不當以及離散點所帶來的影響,更加真實地反映了數據集的結構關系。實驗結果表明,提出的NSG-SC算法具有可行性和有效性。
關鍵詞 譜聚類; 自然最近鄰; 相似圖; 相似度矩陣
基金項目 江蘇省產學研合作項目(BY2015019-30)
本文URL http://www.oirznw.live/article/01-2020-01-006.html
英文標題 Spectral clustering based on natural nearest neighbor similarity graph
作者英文名 Liu Youchao, Zhang Xihuang
機構英文名 School of Internet of Things Engineering,Jiangnan University,Wuxi Jiangsu 214122,China
英文摘要 The traditional spectral clustering algorithm cannot often get correct results on complex data sets, and the choice of parameters of affinity matrix construction depends on multiple tests and personal experience. Based on the situation, this paper proposed a spectral clustering algorithm based on natural nearest neighbor similarity graph(NSG-SC). Natural nearest neighbor was a novel concept in terms of nearest neighbor, and it could avoid the disadvantages of K-nearest neighbor and ε-nearest neighbor. They usually needed set parameters artificially effectively. The algorithm constructed an affinity matrix depending on the characteristics of the data sets, and it avoided some adverse effects. It was that inappropriate choice of parameters and isolated points cause them. The algorithm could also reflect better characteristics of data. The results of experiment show that the proposed algorithm named NSG-SC has feasibility and effectiveness.
英文關鍵詞 spectral clustering; natural nearest neighbor; similarity graph; affinity matrix
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收稿日期 2018/6/20
修回日期 2018/7/31
頁碼 30-33,39
中圖分類號 TP301.6
文獻標志碼 A
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