基于融合歐氏距離與Kendall Tau距離度量的譜聚類算法（英文）
發(fā)布時(shí)間：2018-12-12 21:42
【摘要】：大多數(shù)現(xiàn)存的譜聚類方法均使用傳統(tǒng)距離度量計(jì)算樣本之間的相似性,這樣僅僅考慮了兩兩樣本之間的相似性而忽略了周圍的近鄰信息,更沒有顧及數(shù)據(jù)的全局性分布結(jié)構(gòu).因此,本文提出一種新的融合歐氏距離和Kendall Tau距離的譜聚類方法.該方法通過融合兩兩樣本之間的直接距離以及其周圍的近鄰信息,充分利用了不同的相似性度量可以從不同角度抓取數(shù)據(jù)之間結(jié)構(gòu)信息的優(yōu)勢(shì),更加全面地反映數(shù)據(jù)的底層結(jié)構(gòu)信息.通過與傳統(tǒng)聚類算法在UCI標(biāo)準(zhǔn)數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果作比較,驗(yàn)證了本文的方法可以顯著提高聚類效果.
[Abstract]:Most of the existing spectral clustering methods use the traditional distance measure to calculate the similarity between samples, which only considers the similarity between pairwise samples and neglects the neighboring information, and does not take into account the global distribution structure of the data. Therefore, a new spectral clustering method combining Euclidean distance and Kendall Tau distance is proposed. By combining the direct distance between two samples and the adjacent information around it, the method makes full use of the advantages of different similarity measures to capture the structural information between data from different angles. A more comprehensive reflection of the underlying structure of the data information. By comparing with the experimental results of traditional clustering algorithm on UCI standard data set, it is verified that the proposed method can significantly improve the clustering effect.
【作者單位】： 南京航空航天大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：Supported by National Natural Science Foundation of China(61422204,61473149) Jiangsu Natural Science Foundation for Young Scholar(BK2013-0034) Foundation of Graduate Innovation Center in NUAA(KFJJ20151605)
【分類號(hào)】：TP311.13
[Abstract]:Most of the existing spectral clustering methods use the traditional distance measure to calculate the similarity between samples, which only considers the similarity between pairwise samples and neglects the neighboring information, and does not take into account the global distribution structure of the data. Therefore, a new spectral clustering method combining Euclidean distance and Kendall Tau distance is proposed. By combining the direct distance between two samples and the adjacent information around it, the method makes full use of the advantages of different similarity measures to capture the structural information between data from different angles. A more comprehensive reflection of the underlying structure of the data information. By comparing with the experimental results of traditional clustering algorithm on UCI standard data set, it is verified that the proposed method can significantly improve the clustering effect.
【作者單位】： 南京航空航天大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：Supported by National Natural Science Foundation of China(61422204,61473149) Jiangsu Natural Science Foundation for Young Scholar(BK2013-0034) Foundation of Graduate Innovation Center in NUAA(KFJJ20151605)
【分類號(hào)】：TP311.13
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