This document proposes a crime analysis system using data mining and machine learning techniques. It involves collecting large amounts of unstructured data from various online sources, storing it in a NoSQL database for flexibility, classifying crimes using naive Bayes, identifying patterns using clustering, and potentially adding criminal profiling capabilities in the future. The goal is to develop a more efficient tool for law enforcement to detect crime patterns and help solve cases faster.

1. Abstract
 What is crime analysis ?
 Crime analysis is a law enforcement function that involves systematic
analysis for identifying and analyzing patterns and trends in crime and
disorder.
 The proposed system has an approach between computer science and
criminal justice to develop a data mining procedure that can help solve
crimes faster.

2. Introduction
 It is only within the last few decades that the technology made spatial data
mining a practical solution for wide audiences of law enforcement officials
which is affordable and available.
 Huge chunks of data to be collected-web sites, news sites, blogs, social
media, RSS feeds etc.
 So the main challenge in front of us is developing a better , efficient crime
pattern detection tool to identify crime patterns effectively.

3. Doing analysis is a hard job !
 The reason for the choosing this (clustering)
 Only known data present with us
 Classification technique will not predict well
 Also nature of crimes change over time
 So in order to be able to detect newer and unknown patterns
in future , clustering techniques work better.

4. Steps in doing crime analysis
Data collection
Classification
Pattern
Prediction
visualization

5. Related work
 Serial finder for finding the patterns in burglary.
 For achieving this they used the modus operandi of offender and they
extracted some crime patterns which were followed by offender.
 The algorithm constructs modus operandi of the offender

6. Methodology
 Collection data from various sources like news sites , blogs , social media
RSS feeds etc.
 But the data we got is “very unstructured ” !, and how do we store it ?
 The advantage of NO SQL database is that it allows insertion of data
without a predefined schema
 Object – oriented programming – hence is easy to use and flexible.
 Unlike sql database it not need to know what we are storing in advance
specify its size etc.

7. Methodology
 Classification
 Naïve baye – a supervised learning method as well as a
statistical method
 The algorithm classifies a news article into a crime type to
which it fits the
 Best eg.”what is the probability that a crime document d
belongs to a given class c ?”

8. Future work
 Criminal profiling
 Helps the crime investigators to record the characteristics of criminals
 The main goal of doing criminal profiling is that :
 To provide crime investigatigators with a social and psychological
assessment of the offender
 To evaluate belongs found in the possession of the offender
 For doing this the maximum details of each criminals is collected from
criminal records and the modus operand I is found out

9. Criminal profiling
Root
bbb@exp.com
Bob
bsmith@acme.com
alice@yahoo.net
Alice
person
Email
Email Email
Email
Email
Email
person

10. Advantages
 Helps to prevent crime in society
 System will keep historical record of crime
 System is user friendly
 Saves the time

11. Disadvantages
 Users who don’t have internet connection can not access the system
 Admin must enter correct records otherwise system will provide wrong
information

12. Thank you