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K-means algorithm-based public security crime class case research and judgment method

A technology for public security and similar cases, applied in computing, computer parts, instruments, etc., can solve the problems of manual subjective judgment, low real-time performance, and easy omission of case data, so as to reduce the workload of the police and improve the efficiency of solving cases

Inactive Publication Date: 2017-09-08
NETPOSA TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the following problems will be encountered in the search and positioning of similar cases through manual research and judgment: 1) the types of cases are cumbersome; 2) the amount of case data is large; 3) case data is easy to miss; 4) manual subjective judgment increases the risk of clustering; 5 ) The real-time performance is relatively low, and it is impossible to quickly cluster similar cases; 6) A large amount of manual input greatly increases the workload of front-line police officers

Method used

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  • K-means algorithm-based public security crime class case research and judgment method
  • K-means algorithm-based public security crime class case research and judgment method
  • K-means algorithm-based public security crime class case research and judgment method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052]

[0053]

[0054] The attributes of the above massive historical cases are vectorized:

[0055] It is processed as 6-dimensional feature attributes, combined with the bag of words model through the keyword similarity algorithm (algorithm steps: first calculate each case and briefly explain Case i name in ij Relative to the similarity of the S basic words in the word bag, and select the maximum similarity; then multiply the selected maximum similarity by the keyword name ij The order in the word bag (making it unique)) to calculate the vector value of each dimension, thus forming a case matrix, as follows:

[0056]

[0057] 3) K-value optimization selection in the K-Means algorithm to obtain the optimal K value:

[0058] By determining the K range [1, 100], that is, 1<=k<=100, combined with the silhouette coefficient from 1 to 100, the optimal silhouette coefficient value is calculated cyclically, so as to obtain the K value corresponding to the optimal silhou...

Embodiment 2

[0064] First, collect the information of the client’s public security case that has been solved in the latest time (at least more than 1 month), and perform data processing. After processing the data, the information is as follows: (6 dimensions of the case)

[0065]

[0066]

[0067] The attributes of the above-mentioned massive historical cases are:

[0068] It is processed into 6-dimensional feature vectors, combined with the bag of words model through the keyword similarity algorithm (algorithm steps: first calculate each case and briefly explain Case i name in ij Relative to the similarity of the S basic words in the word bag, and select the maximum similarity; then multiply the selected maximum similarity by the keyword name ij The order in the bag of words (making it unique) calculates the vector value of each dimension to form a case matrix, as follows:

[0069]

[0070] 3) K-value optimization selection in the K-Means algorithm to obtain the optimal K value...

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Abstract

The invention discloses a k-means algorithm-based public security crime class case research and judgment method. The method comprises the steps of collecting information of solved cases in a time at least greater than a month recently from clients, storing the information in a database, and defining 6 dimension vector attributes; extracting case features of the cases, and performing attribute vectorization by utilizing a bag-of-words model to obtain a case matrix; performing clustering by applying a k-means algorithm to form a class case library, taking a mean value of coordinates of all case vectors in each class set as a centroid Ai of the class set, and forming a vector matrix A by centroids of K classes, wherein i is equal to an optimal value of K; and inputting new cases through users, determining eigenvectors of the corresponding cases through the vector attributes defined in the step 1, namely, inputting distances between the case vectors and k class case sets, and pushing the class case set with the shortest distance to case handling policemen, thereby searching for general characteristics of the cases and assisting in case solving. According to the method, class case clustering search and judgment can be performed intelligently, automatically and accurately, so that the workload of the policemen is greatly reduced and the case solving efficiency is improved.

Description

technical field [0001] The invention relates to a method for researching and judging similar public security crime cases based on a k-means algorithm. Background technique [0002] For the cases that occur in the current society, the specific information of the perpetrators cannot be located during the investigation, and other similar cases are clustered into a similar crime type. Through these clustered cases, combined with other information (such as: tourism, travel, If you find co-occurrence personnel, you can land as a key suspect, which is of great significance to solving the case. Now through an intelligent k-means algorithm, similar cases are clustered together without manual subjective judgment. [0003] Through the investigation, it is found that for the clustering of similar cases, similar cases are generally clustered manually through experience, and no advanced intelligent, effective, automated, and efficient clustering research and judgment methods have been in...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06Q50/26
CPCG06Q50/26G06F18/23213G06F18/24137
Inventor 吕品高王忠林
Owner NETPOSA TECH
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