A framework for crime data analysis using relationship among named entities

2019 ◽  
Vol 32 (12) ◽  
pp. 7671-7689 ◽  
Author(s):  
Priyanka Das ◽  
Asit Kumar Das ◽  
Janmenjoy Nayak ◽  
Danilo Pelusi
Keyword(s):  
Data Analysis ◽  
Named Entities ◽  
Crime Data

Crime Data Analysis Using Association Rule Mining Technique

2021 ◽  
pp. 897-907
Author(s):  
Vijendra Kamble ◽  
Aniket Muley ◽  
Nilesh Deshmukh ◽  
Parag Bhalchandra ◽  
Nitin Darkunde ◽  
...  
Keyword(s):  
Data Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Crime Data ◽  
Mining Technique

Survey of Data Mining Techniques on Crime Data Analysis

2012 ◽  
Vol 1 (2) ◽  
pp. 47-49 ◽  
Author(s):  
Revatthy Krishnamurthy ◽  
◽  
J. Satheesh Kumar ◽  
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Mining Techniques ◽  
Crime Data

CRIME DATA ANALYSIS WITH ASSOCIATION RULE MINING

2021 ◽  
Vol 2 (2) ◽  
pp. 42-50
Author(s):  
Duygu ÇALIŞKAN,Buket DOĞAN,Kazım YILDIZ,Abdulsamet AKTAŞ
Keyword(s):  
Data Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Crime Data

scholarly journals Improving Geocoding Rates in Preparation for Crime Data Analysis

2007 ◽  
Vol 9 (1) ◽  
pp. 80-92 ◽  
Author(s):  
Allan J. Brimicombe ◽  
Lily C. Brimicombe ◽  
Yang Li
Keyword(s):  
Data Analysis ◽  
Crime Data

scholarly journals AN APPROACH TO CRIME DATA ANALYSIS: A SYSTEMATIC REVIEW

2020 ◽  
Vol 5 (2) ◽  
pp. 67-74
Author(s):  
Deepika Tyagi ◽  
Dr. Sanjiv Sharma
Keyword(s):  
Data Analysis ◽  
Law Enforcement ◽  
Data Storage ◽  
Criminal Behavior ◽  
Rate Increase ◽  
Crime Control ◽  
Law Enforcement Agencies ◽  
Crime Data ◽  
Analysis Process ◽  
Day By Day

In the current era, number of crimes occurs in the society and this criminal rate increase day by day. There is tremendous growth of criminal data. Crime has negatively influenced the societies. Crime control is essential for the welfare, stability and development of society. Law enforcement agencies are seeking for the system to target crime structure efficiently. The intelligent crime data analysis provides the best understanding of the dynamics of unlawful activities, discovering patterns of criminal behavior that will be useful to understand where, when and why crimes can occur. There is a need for the advancements in the data storage collection, analysis and algorithm that can handle data and yield high accuracy. This paper demonstrates the data mining technologies which are used in criminal investigation. The contribution of this paper is to highlight the methodology used in crime data analytics. This paper summarizes the challenges arising during the analysis process, which should be removed to get the desired result.                                      

scholarly journals Occupational profiling driven by online job advertisements: Taking the data analysis and processing engineering technicians as an example

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253308
Author(s):  
Lina Cao ◽  
Jian Zhang ◽  
Xinquan Ge ◽  
Jindong Chen
Keyword(s):  
Data Analysis ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Survey Method ◽  
Named Entities ◽  
Named Entity ◽  
Occupational Information ◽  
Multiple Dimensions ◽  
Job Advertisements ◽  
Similarity Algorithm

The occupational profiling system driven by the traditional survey method has some shortcomings such as lag in updating, time consumption and laborious revision. It is necessary to refine and improve the traditional occupational portrait system through dynamic occupational information. Under the circumstances of big data, this paper showed the feasibility of vocational portraits driven by job advertisements with data analysis and processing engineering technicians (DAPET) as an example. First, according to the description of occupation in the Chinese Occupation Classification Grand Dictionary, a text similarity algorithm was used to preliminarily choose recruitment data with high similarity. Second, Convolutional Neural Networks for Sentence Classification (TextCNN) was used to further classify the preliminary corpus to obtain a precise occupational dataset. Third, the specialty and skill were taken as named entities that were automatically extracted by the named entity recognition technology. Finally, putting the extracted entities into the occupational dataset, the occupation characteristics of multiple dimensions were depicted to form a profile of the vocation.

scholarly journals Crime Data Analysis Using Pig with Hadoop

2016 ◽  
Vol 78 ◽  
pp. 571-578 ◽  
Author(s):  
Arushi Jain ◽  
Vishal Bhatnagar
Keyword(s):  
Data Analysis ◽  
Crime Data
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