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Crime Investigation

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International Journal For Technological Research In Engineering
Volume 1, Issue 9, May-2014

ISSN (Online): 2347 - 4718

DATA MINING TECHNIQUES TO ANALYZE CRIME DATA
R. G. Uthra, M. Tech (CS)
Bharathidasan University, Trichy, India.
Abstract: In data mining, Crime management is an interesting application where it plays an important role in handling of crime data. Crime investigation has very significant role of police system in any country. There had been an enormous increase in the crime in recent years.
With rapid popularity of the internet, crime information maintained in web is becoming increasingly rampant. In this paper the data mining techniques are used to analyze the web data. This paper presents detailed study on classification and clustering. Classification is the process of classifying the crime type Clustering is the process of combining data object into groups. The construct of scenario is to extract the attributes and relations in the web page and reconstruct the scenario for crime mining.
Key words: Crime data analysis, classification, clustering.
I. INTRODUCTION
Crime is one of the dangerous factors for any country. Crime analysis is the activity in which analysis is done on crime activities. Today criminals have maximum use of all modern technologies and hi-tech methods in committing crimes. The law enforcers have to effectively meet out challenges of crime control and maintenance of public order. One challenge to law enforcement and intelligence agencies is the difficulty of analyzing large volumes of data involved in criminal and terrorist activities. Hence, creation of data base for crimes and criminals is needed. Data mining holds the promise of making it easy, convenient and practical to explore very large databases for organizations and users.
Developing a good crime analysis tool to identify crime patterns quickly and efficiently for future crime pattern detection is challenging field for researchers. A web page involving a crime can be thought of as a chain of actions with series of background attributes. We can analyze web information from the perspective of events and apply some research results related to the events to solve the problem of web crime mining.
II. RELATED WORK
It is important to study the previous related works to both learn from the experience of others and to add something to our existing body of knowledge. Existing literature has been reviewed in three different areas: crime data mining, data extraction and data focus
Crime Data Mining: Data mining is defined as the discovery of interesting structure in data, where structure designates patterns, statistical or predictive models of the data, and relationships among parts of the data. The data mining techniques is using for some results on crime mining. This technique is applied

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to study crime cases, which mainly concerned entity extraction, pattern clustering, classification and social network analysis. This method used to get the data of criminals by using frequency occurrence of incidents.
Data Extraction Event: In web page extraction is the process to extract attributes and relationship. The idea of this event extraction is the method of retrieving the information from database. The proposed a method to append events for the concept of data mining techniques. Entity extraction has been used to automatically identify person, address, cases, and personal properties from police reports to the judge.
Data Focus: This research paper is on web mining of content, using clustering techniques the web mining focus on the text. In data focus clustering will convert nonlinear statistical relationship between high dimensional data into simple geometrical relationship in low dimensional display.
III. METHODOLOGY
In this section, discuss about the methodology for the research. Data Collection: The data set is the collection of field in the data from web pages on the internet. The data set which consists of the text from web pages and the pictures, videos or sound format will be ignored.
Preprocessing: A data preprocessing is a process that consists of data cleaning, data integration and data transformation which is usually processed by a computer program. It intends to reduce some noises, incomplete and inconsistent data. The results from preprocessing step can be later proceeding by data mining algorithm.
Clustering: Then the clustering techniques are used to fetch the information of criminals.
IV. OVERVIEW OF CRIME DATA MINING
Data Mining: Data mining deals with the discovery of unexpected patterns and new rules that are “hidden” in large databases. The use of data mining in this paper is to give the structured data from unstructured data of judge. In this paper the Data Mining techniques of crime in two directions they are 1. Classification of Crime
2. Clustering Technique of Crime
1. Classification of Crime
Crime: Crime is defined as “an act or the commission of an act that is forbidden, or the omission of a duty that is

Copyright 2013.All rights reserved.

882

International Journal For Technological Research In Engineering
Volume 1, Issue 9, May-2014

ISSN (Online): 2347 - 4718

commanded by a public law and that makes the offender liable to punishment by that law”. Crime is referred to as a comprehensive concept that is defined in both legal and nonlegal sense.

Step 3: Re-compute the new cluster centers
Repetition step: Repeat steps 3 and 4 until some convergence criterion is met (usually that the assignment of points to clusters becomes stable).

Classification of Crime
 Traffic Violations
 Sex Crime
 Fraud
 Arson
 Drug Offences
 Violent Crime
 Cyber Crime

V. RESULT
The result of this crime management system is given below:
Judgement of a Crime
Table 1—Unstructured data case no 011 Bala kumar is sentenced to jail for 14 years in the palaiyankotai prison for the violent crime of a person using knife.

Traffic Violations: Driving under the influence of alcohol, fatal / personal injury / property damage traffic accident, road rage Sex Crime: Sexual offences
Fraud: Forgery and counterfeiting, frauds, embezzlement, identity deception
Arson: Arson on buildings
Drug Offences: Narcotic drug offences (sales or possession)
Violent Crime: Criminal Homicide, armed robbery, aggravated assault, other assaults
Cyber Crime: Internet frauds, illegal trading, network intrusion / hacking, virus spreading, hate crimes, cyber piracy, cyber pornography, cyber-terrorism, theft of confidential information
2. Clustering Technique of Crime
Clustering: Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. Clustering can also be considered the most important unsupervised learning technique; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. There are so many techniques used in clustering, in this paper only K-means algorithm is used.
K-Means Clustering Algorithm: K-means algorithm mainly used to partition the clusters based on their means. Initially number of objects are grouped and specified as K clusters.
The algorithm clusters observations into K groups, where K is provided as an input parameter. It then assigns each observation to clusters based upon the observation proximity to the mean of the cluster. The cluster’s mean is then recomputed and the process begins again. In this paper the use of K-means algorithm is the process of getting a structured data from a unstructured data. The working of algorithm is explained as follows:
 k : pre-determined number of clusters
 Algorithm (Step 0: determine value of k)
Step 1: Randomly generate k random points as initial cluster centers Step 2: Assign each point to the nearest cluster center

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Prison Admin
Case no

Table 2—Structured data
011

Name

Bala Kumar

Crime Type

Violent crime

Judgment

14 years

Location

Palaiyankotai

The use of K-means clustering algorithm in this paper is to get the structured data from unstructured data from the data base. The prediction of this retrieving information from database is clear and understands to the user of the system.
Predict the name, case, judgment, etc. from the judge. The judge gives the unstructured information of a criminal as judgment. The prisoner admin get the data of particular criminal information in a structured format.
VI. CONCLUSION
Crime data is a sensitive domain where effective data mining techniques play a vital role for crime analysis. In this paper the classify and cluster techniques are used to analyse the crime data from database. This technique is faster to get the data through web; the effective web mining is to get the unstructured data to structured data. The classifications of crime type are violent, arson, fraud, etc. and clustering of crime using k-means to retrieve the data.
REFERENCES
[1] Anshu Sharma, Shilpa Sharma, An Intelligent Analysis of web Crime Data Using Data Mining, Internaution
Journal of Engineering and Innovative Technology,
2012.
[2] S. Yamuna, N. Sudha Bhuvaneswari, Data Mining
Technique to Analyse and Predict Crime, The
International Journal of Engineering And Science, 2012.
[3] Revathy Krishnamurthy, J. Satheesh Kumar, Survey of
Data Mining Techniques on Crime Data Analysis,
International Journal of Data Mining Techniques and
Applications, 2012.
[4] Okwangle Fredrick R, Survey of Data Mining Methods for Crime Analysis and Visualization, ICT and Legal

Copyright 2013.All rights reserved.

883

International Journal For Technological Research In Engineering
Volume 1, Issue 9, May-2014

ISSN (Online): 2347 - 4718

Applications 221, 2012
[5] Malathi. A, Dr. S. Santhosh Baboo, Anbarasi. A, An
Intelligent Analysis of a City Crime Data Using Data
Mining, Internation Conference on Information and
Electronics Engineering, 2011
[6] Devesh Bajpai, Emerging Trends in Utilization of Data
Mining in Criminal Investigation: An Overview, Journal of Environmental Science, Computer Science and
Engineering & Technology, 2012
[7] Neha Gohar Khan, Prof. V. B. Bhagat, Effective Data
Mining Approach for Crime-Terror pattern Detection
Using Clustering Algorithm Technique, International
Journal of Engineering Research & Technology, 2013.
[8] Neelamadhab Padhy, Dr. Pragnyaban Mishra, and
Rasmita Panigrahi, The Survey of Data Mining
Applications And Feature Scope, International Journal of
Computer Science, Engineering and Information
Technology, 2012.
[9] Manish Verma, Mauly Srivastava, Neha Chack, Atul
Kumar Diswar, Nidhi Gupta , A Comparative Study of
Various Clustering Algorithms in Data Mining,
International Journal of Engineering Research and
Applications, 2012.
[10] Ruijuan Hu, Data Mining in the Application of Criminal
Cases Based on Decision Tree, International Journal of
Engineering Sciences, 2013.

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