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An interactive human centered data science approach towards crime pattern analysis

机译：一种交互式的以人为本的数据科学方法来进行犯罪模式分析

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The traditional machine learning systems lack a pathway for a human to integrate their domain knowledge into the underlying machine learning algorithms. The utilization of such systems, for domains where decisions can have serious consequences (e.g. medical decision-making and crime analysis), requires the incorporation of human experts' domain knowledge. The challenge, however, is how to effectively incorporate domain expert knowledge with machine learning algorithms to develop effective models for better decision making.In crime analysis, the key challenge is to identify plausible linkages in unstructured crime reports for the hypothesis formulation. Crime analysts painstakingly perform time-consuming searches of many different structured and unstructured databases to collate these associations without any proper visualization. To tackle these challenges and aiming towards facilitating the crime analysis, in this paper, we examine unstructured crime reports through text mining to extract plausible associations. Specifically, we present associative questioning based searching model to elicit multi-level associations among crime entities. We coupled this model with partition clustering to develop an interactive, human-assisted knowledge discovery and data mining scheme.The proposed human-centered knowledge discovery and data mining scheme for crime text mining is able to extract plausible associations between crimes, identifying crime pattern, grouping similar crimes, eliciting co-offender network and suspect list based on spatial-temporal and behavioral similarity. These similarities are quantified through calculating Cosine, Jacquard, and Euclidean distances. Additionally, each suspect is also ranked by a similarity score in the plausible suspect list. These associations are then visualized through creating a two-dimensional re-configurable crime cluster space along with a bipartite knowledge graph.This proposed scheme also inspects the grand challenge of integrating effective human interaction with the machine learning algorithms through a visualization feedback loop. It allows the analyst to feed his/her domain knowledge including choosing of similarity functions for identifying associations, dynamic feature selection for interactive clustering of crimes and assigning weights to each component of the crime pattern to rank suspects for an unsolved crime.We demonstrate the proposed scheme through a case study using the Anonymized burglary dataset. The scheme is found to facilitate human reasoning and analytic discourse for intelligence analysis.
机译：传统的机器学习系统缺少人将其领域知识集成到基础机器学习算法中的途径。在决策可能会产生严重后果的领域（例如医疗决策和犯罪分析）中使用此类系统需要整合人类专家的领域知识。然而，挑战在于如何有效地将领域专家知识与机器学习算法相结合，以开发有效的模型以更好地制定决策。在犯罪分析中，关键挑战是在非结构化犯罪报告中为假设的制定识别合理的联系。犯罪分析师费力地对许多不同的结构化和非结构化数据库进行耗时的搜索，以在没有任何适当可视化的情况下整理这些关联。为了应对这些挑战并旨在促进犯罪分析，在本文中，我们通过文本挖掘来研究非结构化犯罪报告，以提取合理的关联。具体而言，我们提出了基于关联询问的搜索模型，以在犯罪实体之间引发多层次的关联。我们将此模型与分区聚类结合起来，以开发一种交互式的，人类辅助的知识发现和数据挖掘方案。所提出的以人为中心的知识发现和数据挖掘方案用于犯罪文本挖掘能够提取犯罪之间的合理关联，识别犯罪模式，将相似的犯罪进行分组，根据时空和行为相似性，得出共同犯罪者网络和犯罪嫌疑人名单。通过计算余弦，提花和欧几里得距离来量化这些相似性。此外，每个嫌疑人还会在合理的嫌疑人列表中按照相似度评分进行排名。然后通过创建二维可重配置犯罪集群空间以及两方知识图来可视化这些关联。该提议的方案还通过可视化反馈回路来检验将有效的人机交互与机器学习算法集成在一起的巨大挑战。它使分析人员能够提供自己的领域知识，包括选择相似性函数以识别关联，动态特征选择以交互犯罪组合以及为犯罪模式的每个组成部分分配权重以对未解决犯罪的嫌疑人进行排名。通过使用匿名入室盗窃数据集的案例研究来制定方案。发现该方案有助于人类推理和情报分析的分析话语。

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