快速識別密度骨架的聚類算法
發(fā)布時間：2018-11-02 16:24
【摘要】：針對如何快速尋找密度骨架、提高高維數(shù)據(jù)聚類準(zhǔn)確性的問題,提出一種快速識別高密度骨架的聚類(ECLUB)算法。首先,在定義了對象局部密度的基礎(chǔ)上,根據(jù)互k近鄰一致性及近鄰點局部密度關(guān)系,快速識別出高密度骨架;然后,對未分配的低密度點依據(jù)鄰近關(guān)系進(jìn)行劃分,得到最終聚類。人工合成數(shù)據(jù)集及真實數(shù)據(jù)集上的實驗驗證了所提算法的有效性,在Olivetti Face數(shù)據(jù)集上的聚類結(jié)果顯示,ECLUB算法的調(diào)整蘭德系數(shù)(ARI)和歸一化互信息(NMI)分別為0.877 9和0.962 2。與經(jīng)典的基于密度的聚類算法(DBSCAN)、密度中心聚類算法(CFDP)以及密度骨架聚類算法(CLUB)相比,所提ECLUB算法效率更高,且對于高維數(shù)據(jù)聚類準(zhǔn)確率更高。
[Abstract]:To solve the problem of how to find density skeleton quickly and improve the accuracy of high dimensional data clustering, a fast clustering (ECLUB) algorithm for high density skeleton recognition is proposed. Firstly, based on the definition of the local density of the object, the high density skeleton is quickly identified according to the mutual k-nearest neighbor consistency and the local density relation of the nearest neighbor. Then, the unallocated low density points are divided according to the neighborhood relationship, and the final clustering is obtained. Experiments on synthetic data sets and real data sets verify the effectiveness of the proposed algorithm. The clustering results on Olivetti Face datasets show that, The adjusted Rand coefficient (ARI) and normalized mutual information (NMI) of ECLUB algorithm are 0.877 9 and 0.962 2 respectively. Compared with the classical density-based clustering algorithm (DBSCAN), density center clustering algorithm (CFDP) and density skeleton clustering algorithm (CLUB), the proposed ECLUB algorithm is more efficient and accurate for high-dimensional data clustering.
【作者單位】： 鄭州大學(xué)信息工程學(xué)院;
【基金】：河南省基礎(chǔ)與前沿基金資助項目(152300410191)~~
【分類號】：TP311.13
[Abstract]:To solve the problem of how to find density skeleton quickly and improve the accuracy of high dimensional data clustering, a fast clustering (ECLUB) algorithm for high density skeleton recognition is proposed. Firstly, based on the definition of the local density of the object, the high density skeleton is quickly identified according to the mutual k-nearest neighbor consistency and the local density relation of the nearest neighbor. Then, the unallocated low density points are divided according to the neighborhood relationship, and the final clustering is obtained. Experiments on synthetic data sets and real data sets verify the effectiveness of the proposed algorithm. The clustering results on Olivetti Face datasets show that, The adjusted Rand coefficient (ARI) and normalized mutual information (NMI) of ECLUB algorithm are 0.877 9 and 0.962 2 respectively. Compared with the classical density-based clustering algorithm (DBSCAN), density center clustering algorithm (CFDP) and density skeleton clustering algorithm (CLUB), the proposed ECLUB algorithm is more efficient and accurate for high-dimensional data clustering.
【作者單位】： 鄭州大學(xué)信息工程學(xué)院;
【基金】：河南省基礎(chǔ)與前沿基金資助項目(152300410191)~~
【分類號】：TP311.13
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