Crime Data Mining, Threat Analysis and Prediction

2018 ◽  
pp. 183-202 ◽  
Author(s):  
Maryam Farsi ◽  
Alireza Daneshkhah ◽  
Amin Hosseinian Far ◽  
Omid Chatrabgoun ◽  
Reza Montasari
Keyword(s):  
Data Mining ◽  
Crime Data ◽  
Threat Analysis

Internal and External Threat Analysis of Anonymized Dataset

2020 ◽  
pp. 172-185
Author(s):  
Saurav Jindal ◽  
Poonam Saini
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Information Gain ◽  
Threat Analysis ◽  
External Threat ◽  
Utility Of Information ◽  
Cost Penalty ◽  
Computational Processes ◽  
Different Sources ◽  
Unusual Situation

In recent years, data collection and data mining have emerged as fast-paced computational processes as the amount of data from different sources has increased manifold. With the advent of such technologies, major concern is exposure of an individual's self-contained information. To confront the unusual situation, anonymization of dataset is performed before being released into public for further usage. The chapter discusses various existing techniques of anonymization. Thereafter, a novel redaction technique is proposed for generalization to minimize the overall cost (penalty) of the process being inversely proportional to utility of generated dataset. To validate the proposed work, authors assume a pre-processed dataset and further compare our algorithm with existing techniques. Lastly, the proposed technique is made scalable thus ensuring further minimization of generalization cost and improving overall utility of information gain.

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

scholarly journals The Data Mining Support Vector Machine Algorithm used for Detecting and Forecasting of Crimes

2019 ◽  
Vol 9 (1) ◽  
pp. 395-401
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Mining System ◽  
Data Mining Techniques ◽  
Crime Data ◽  
Data Archives ◽  
Crime Investigation ◽  
Chicago Police

Crime rate is expanding extremely more because of destitution and joblessness. With the current crime investigation techniques, officers need to invest a great deal of energy just as labor to recognize suspects and criminals. Anyway crime investigation procedure should be quicker and dynamic. As huge amount of data is gathered during crime investigation, data mining is a methodology which can be valuable in this viewpoint. Data mining is a procedure that concentrates valuable data from enormous amount of crime data with the goal that potential suspects of the crime can be recognized productively. Quantities of data mining techniques are accessible. Utilization of specific data mining system has more prominent impact on the outcomes acquired. So the exhibition of three data mining techniques will be analyzed against test crime and criminal database and best performing algorithm will be utilized against test crime and criminal database to recognize potential suspects of the crime. Data mining is a procedure of separating information from colossal amount of data put away in databases, data stockrooms and data archives. Clustering is the way toward consolidating data objects into gatherings. Here taken the Crime dataset from Chicago police website and implemented in MATLAB utilizing Support Vector Machine algorithm.

scholarly journals A Method for finding threated web sites through crime data mining and sentiment analysis

2018 ◽  
Vol 7 (2.7) ◽  
pp. 62 ◽  
Author(s):  
K V. Daya Sagar ◽  
Ch Shyam Krishna ◽  
G Lalith Kumar ◽  
P Surya Teja ◽  
G Charless Babu
Keyword(s):  
Data Mining ◽  
Sentiment Analysis ◽  
Web Sites ◽  
Crime Data

This paper has been withdrawn.

Ontology-Based Interpretation and Validation of Mined Knowledge

2008 ◽  
pp. 2316-2337 ◽  
Author(s):  
Ana Isabel Canhoto
Keyword(s):  
Data Mining ◽  
Social Norms ◽  
Domain Knowledge ◽  
Government Agencies ◽  
Access To Information ◽  
Automated Systems ◽  
Crime Data ◽  
War On Crime ◽  
Output Domain

The use of automated systems to collect, process and analyse vast amounts of data is now integral to the operations of many corporations and government agencies, in particular it has gained recognition as a strategic tool in the war on crime. Data mining, the technology behind such analysis, has its origins in quantitative sciences. Yet, analysts face important issues of a cognitive nature both in terms of the input for the data mining effort, and in terms of the analysis of the output. Domain knowledge and bias information influence which patterns in the data are deemed as useful and, ultimately, valid. This chapter addresses the role of cognition and context in the interpretation and validation of mined knowledge. We propose the use of ontology charts and norm specifications to map how varying levels of access to information and exposure to specific social norms lead to divergent views of mined knowledge.

Ontology-Based Interpretation and Validation of Mined Knowledge

2011 ◽  
pp. 84-105 ◽  
Author(s):  
Ana Isabel Canhoto
Keyword(s):  
Data Mining ◽  
Social Norms ◽  
Domain Knowledge ◽  
Government Agencies ◽  
Access To Information ◽  
Automated Systems ◽  
Crime Data ◽  
War On Crime ◽  
Output Domain

The use of automated systems to collect, process and analyse vast amounts of data is now integral to the operations of many corporations and government agencies, in particular it has gained recognition as a strategic tool in the war on crime. Data mining, the technology behind such analysis, has its origins in quantitative sciences. Yet, analysts face important issues of a cognitive nature both in terms of the input for the data mining effort, and in terms of the analysis of the output. Domain knowledge and bias information influence which patterns in the data are deemed as useful and, ultimately, valid. This chapter addresses the role of cognition and context in the interpretation and validation of mined knowledge. We propose the use of ontology charts and norm specifications to map how varying levels of access to information and exposure to specific social norms lead to divergent views of mined knowledge.

scholarly journals Crime analysis in India using data mining techniques

2018 ◽  
Vol 7 (2.6) ◽  
pp. 253 ◽  
Author(s):  
Deepika K K ◽  
Smitha Vinod
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Random Forest ◽  
Crime Analysis ◽  
Random Forest Algorithm ◽  
Indian States ◽  
Data Mining Techniques ◽  
Crime Data ◽  
Crime Detection ◽  
Using Data

An approach for crime detection in India using Data mining techniques is proposed in this paper. The approach consists of the following steps - Data pre-processing, clustering, classification and visualization. Data mining techniques are often applied to Criminology as it provides good results. Criminology is a field which studies about various crime characteristics. Analyzing crime data means exploring crime data. Crime is identified using k-means clustering and the clusters are formed based on the similarity of the crime attributes. The Random Forest algorithm and Neural networks are applied on the data for classification. Visualization is achieved using the Google marker clustering and the crime spots are marked on the India map. The accuracy is verified using WEKA tool. This approach will benefit the Crime department of India in analyzing crime with better prediction. The paper focuses on the crime analysis of various Indian states and union territories during 2001 to 2012.  

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