【摘要】：針對現(xiàn)有的co-location模式挖掘算法無法有效處理不均勻分布空間對象的問題,提出一種不均勻模糊空間對象的分層次co-location模式挖掘方法。首先提出一種不均勻數(shù)據(jù)集的生成方法;然后對不均勻分布的數(shù)據(jù)集進(jìn)行層次劃分,使每個(gè)區(qū)域具有均勻的空間分布;再基于改進(jìn)的PO_RI_PC算法對劃分后的模糊對象進(jìn)行空間數(shù)據(jù)挖掘。該方法基于距離變化系數(shù)構(gòu)建每個(gè)子區(qū)域的鄰域關(guān)系圖,進(jìn)而完成區(qū)域融合,實(shí)現(xiàn)co-location模式挖掘。實(shí)驗(yàn)結(jié)果表明,與傳統(tǒng)方法相比,所提方法的執(zhí)行效率更高,隨實(shí)例個(gè)數(shù)和不均勻度的變化獲得的co-location集個(gè)數(shù)更多,同比情況下平均提高約25%,獲得了更精確的挖掘結(jié)果。
[Abstract]:In order to solve the problem that the existing co-location pattern mining algorithms can not effectively deal with uneven distributed spatial objects, a hierarchical co-location pattern mining method for inhomogeneous fuzzy spatial objects is proposed. Firstly, a generation method of uneven data set is proposed, and then the uneven distribution data set is divided into layers so that each region has uniform spatial distribution. Then based on the improved PO_RI_PC algorithm, the spatial data mining of the divided fuzzy objects is carried out. Based on the distance variation coefficient, the neighborhood diagram of each sub-region is constructed, and then the region fusion is completed to realize co-location pattern mining. The experimental results show that compared with the traditional method, the proposed method has higher execution efficiency, and more co-location sets are obtained with the change of the number of examples and inhomogeneity, and the average increase is about 25% compared with the same period last year, and more accurate mining results are obtained.
【作者單位】： 安徽師范大學(xué)數(shù)學(xué)計(jì)算機(jī)科學(xué)學(xué)院;安徽師范大學(xué)國土資源與旅游學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(61370050,61572036) 安徽省自然科學(xué)基金資助項(xiàng)目(1508085QF134) 安徽師范大學(xué)創(chuàng)新基金資助項(xiàng)目(2016XJJ074)~~
【分類號】：TP311.13
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本文編號：2496638