一种无先验信息的模糊聚类算法

Changjun Fan, Kaiming Xiao, Bao-Xin Xiu, Guodong Lv
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引用次数: 9

摘要

犯罪分析已经得到了广泛的研究,但通过通信网络分析来识别共谋者的问题还没有得到很好的解决。本文提出了一种不使用个体先验身份信息的模糊聚类算法来检测主题网络中的隐藏罪犯。首先根据节点的相邻信息(节点和边缘)建立局部怀疑度计算;然后利用全局信息,采用模糊k-均值聚类算法,将可疑组的隶属度作为全局怀疑度。实验结果表明,该方法具有较好的识别效果,已知嫌疑人的识别值相对较高,而已知无辜者的识别值相对较低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A fuzzy clustering algorithm to detect criminals without prior information
Crime analysis has been widely studied, but problem of identifying conspirators through communication network analysis is still not well resolved. In this paper, we proposed a fuzzy clustering algorithm to detect hidden criminals from topic network, which took no use of individuals' prior identity information. We first built up a local suspicion calculation from nodes' neighboring information (node and edge); and then with global information, we employed the fuzzy k-means clustering algorithm, and made the membership to suspicious group as the global suspicion degree. Experiments showed it works well on identification: known suspects gained relative high values and known innocents got relative low values.
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