折半聚類算法在基于社會力的人群疏散仿真中的應(yīng)用
發(fā)布時間：2018-07-29 18:27
【摘要】：運(yùn)用社會力模型(SFM)模擬人群疏散之前,需要先對人群進(jìn)行聚類分組;然而,k中心聚類(k-medoids)和統(tǒng)計信息網(wǎng)格聚類(STING)這兩大傳統(tǒng)聚類算法,在聚類效率和準(zhǔn)確率上都不能滿足要求。針對這個問題,提出了折半聚類算法(BCA)。該算法結(jié)合了圍繞中心點聚類和基于網(wǎng)格聚類兩類方式,并利用二分法查找思想劃分網(wǎng)格,不需要反復(fù)聚類。先將數(shù)據(jù)用二分法劃分成網(wǎng)格,再根據(jù)網(wǎng)格內(nèi)數(shù)據(jù)密度選出核心網(wǎng)格,接著以核心網(wǎng)格為中心將鄰居網(wǎng)格聚類,最后按就近原則歸并剩余網(wǎng)格。實驗結(jié)果表明,在聚類時間上,BCA平均僅是STING算法的48.3%,不到k-medoids算法的14%;而在聚類準(zhǔn)確率上,k-medoids算法平均僅是BCA的50%,STING算法平均也只是BCA的88%。因此,BCA無論在效率還是準(zhǔn)確率上都明顯優(yōu)于STING和k-medoids算法。
[Abstract]:The social force model (SFM) is used to simulate the evacuation of people, but the traditional clustering algorithms such as k-medoids and (STING) can not meet the requirements of clustering efficiency and accuracy. In order to solve this problem, a reduced half clustering algorithm (BCA).) is proposed. The algorithm combines the clustering around the center and the grid clustering, and uses the idea of dichotomy to divide the grid without the need of repeated clustering. Firstly, the data is divided into grids by dichotomy, then the core grid is selected according to the data density in the grid, then the neighbor grid is clustered around the core grid, and the remaining grid is merged according to the principle of proximity. The experimental results show that the average clustering time is only 48.3% of the STING algorithm, less than 14% of the k-medoids algorithm, while the average clustering accuracy of the k-medoids algorithm is only 50% of the BCA algorithm, and the average is only 88% of the BCA algorithm. Therefore, BCA is superior to STING and k-medoids in efficiency and accuracy.
【作者單位】： 山東師范大學(xué)信息科學(xué)與工程學(xué)院;山東省分布式計算機(jī)軟件新技術(shù)重點實驗室;
【基金】：國家自然科學(xué)基金資助項目(61472232,61373149,61572299,61402269) 山東省自然科學(xué)基金資助項目(ZR2014FQ009)~~
【分類號】：TP311.13;U491.226
本文編號：2153608
[Abstract]:The social force model (SFM) is used to simulate the evacuation of people, but the traditional clustering algorithms such as k-medoids and (STING) can not meet the requirements of clustering efficiency and accuracy. In order to solve this problem, a reduced half clustering algorithm (BCA).) is proposed. The algorithm combines the clustering around the center and the grid clustering, and uses the idea of dichotomy to divide the grid without the need of repeated clustering. Firstly, the data is divided into grids by dichotomy, then the core grid is selected according to the data density in the grid, then the neighbor grid is clustered around the core grid, and the remaining grid is merged according to the principle of proximity. The experimental results show that the average clustering time is only 48.3% of the STING algorithm, less than 14% of the k-medoids algorithm, while the average clustering accuracy of the k-medoids algorithm is only 50% of the BCA algorithm, and the average is only 88% of the BCA algorithm. Therefore, BCA is superior to STING and k-medoids in efficiency and accuracy.
【作者單位】： 山東師范大學(xué)信息科學(xué)與工程學(xué)院;山東省分布式計算機(jī)軟件新技術(shù)重點實驗室;
【基金】：國家自然科學(xué)基金資助項目(61472232,61373149,61572299,61402269) 山東省自然科學(xué)基金資助項目(ZR2014FQ009)~~
【分類號】：TP311.13;U491.226
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