Concept Discovery Innovations in Law Enforcement: A Perspective

Jonas Poelmans, P. Elzinga, Stijn Viaene, G. Dedene
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引用次数: 7

Abstract

In the past decades, the amount of information available to law enforcement agencies has increased significantly. Most of this information is in textual form, however analyses have mainly focused on the structured data. In this paper, we give an overview of the concept discovery projects at the Amsterdam-Amstell and police where Formal Concept Analysis (FCA) is being used as text mining instrument. FCA is combined with statistical techniques such as Hidden Markov Models (HMM) and Emergent Self Organizing Maps (ESOM). The combination of this concept discovery and refinement technique with statistical techniques for analyzing high-dimensional data not only resulted in new insights but often in actual improvements of the investigation procedures.
执法中的概念发现创新:一个视角
在过去的几十年里,执法机构可以获得的信息量大大增加。这些信息大多是文本形式的,但是分析主要集中在结构化数据上。在本文中,我们概述了阿姆斯特丹-阿姆斯特尔和警察的概念发现项目,其中形式概念分析(FCA)被用作文本挖掘工具。FCA与隐马尔可夫模型(HMM)和涌现自组织图(ESOM)等统计技术相结合。这种概念发现和精化技术与分析高维数据的统计技术相结合,不仅产生了新的见解,而且经常对调查程序进行实际改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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