DBSCAN clustering algorithm-based method for checking missing registration of key personnel and houses

Abstract

The invention relates to a DBSCAN clustering algorithm-based method for checking missing registration of important personnel and houses, which comprises the following steps of: preprocessing population and house data sets collected by civil police, including missing value filling, category type variable discretization and numerical type variable standardization; adopting a DBSCAN clustering algorithm to classify samples of non-core points on a 'key persons and houses' data set, and analyzing the clustering result; fixing core points of data of all labels' key persons and houses', classifying samples of non-core points on a population and house data set through a DBSCAN clustering algorithm with adaptive characteristic weights, obtaining a clustering result, and finally generating a suspected missed registration 'important person and house' check table. Therefore, the labeled key concerned persons and houses are taken as the core, persons and houses similar to the key persons and the houses are screened out through the density clustering algorithm, then the checking range of suspected key persons and houses is narrowed, and the police checking precision and efficiency can be effectively improved.

Description

Technical field [0001] The invention relates to a method for checking the missing registration of key personnel and houses, in particular to a method for checking the missing registration of key personnel and houses based on a DBSCAN clustering algorithm. Background technique [0002] In the era of big data, data mining technology has played a huge role in many fields. Using big data and algorithms to optimize the traditional community policing verification work model to improve the quality of the work of the grassroots police. The first area to enter is the field of public security. Public security data is not only massive, but also rich in types. There are both traditional structured data and a large amount of unstructured data. The population and housing management work is heavy, and the traditional police inspection work method has been unable to satisfy the public security population and housing business under such a large population and housing base. [0003] In view of the...

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Problems solved by technology

[0004] However, the core points obtained by the classic density clustering algorithm include all points that meet the neighborhood conditions. In the problem of population and housing clustering, non-key personnel and houses are used as core points, and the clustering effect is not satisfactory. In addition, different Assigning equal weights to features in the process of calculating similarity by distance will weaken those feature attributes that are strongly related to key personnel and houses, resulting in misclustering

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