發(fā)布時間：2018-04-30 21:39

  本文選題：群體發(fā)現(xiàn) + 關(guān)鍵圖譜�。� 參考：《浙江大學(xué)學(xué)報(工學(xué)版)》2017年06期

【摘要】：為了在公安情報場景下將人的行為特征量化聚類,從而發(fā)現(xiàn)行為特征相似的人群并將其歸類以提供決策支持,提出一種基于關(guān)鍵圖譜的群體發(fā)現(xiàn)算法(KCD).KCD從人的行為特征入手,通過建立關(guān)鍵圖譜并利用圖聚類算法來進(jìn)行群體發(fā)現(xiàn).KCD首先將人與人之間的多個維度的行為特征進(jìn)行量化計算,并將多維行為特征的量化值融合,形成三元組"人-人-值"的共現(xiàn)度集合;然后過濾噪音數(shù)據(jù),建立基于行為特征的無向圖;最后應(yīng)用聚類算法SCAN從無向圖中找出多個不同的群體,同時找出圖的中心點和離群點,解決了公安情報場景中群體之間關(guān)鍵人物的挖掘問題.
[Abstract]:In order to quantify the behavior characteristics of people in public security information scene, we find people with similar characteristics and classify them to provide decision support, and propose a group discovery algorithm based on key map (KCD).KCD from the behavior characteristics of human, by establishing key linkage map and using graph clustering algorithm for group discovery.K CD first quantifies the behavior characteristics of multiple dimensions between human and human, and combines the quantized values of multi-dimensional behavior features to form a concurrence set of "human to human value" of three tuples, then filters noise data and establishes an undirected graph based on behavior characteristics. Finally, the clustering algorithm SCAN is used to find many different groups from the undirected graph. At the same time, we find out the central point and outlier of the graph, and solve the problem of mining key figures among public security intelligence scenes.

【作者單位】： 華中科技大學(xué)武漢光電國家實驗室;
【基金】：國家自然科學(xué)基金青年基金項目(61502189)
【分類號】：D631;TP311.13
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本文編號：1826372