학술논문

A novel approach for intelligent crime pattern discovery and prediction
Document Type
Conference
Source
2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT) Advanced Communication Control and Computing Technologies (ICACCCT), 2016 International Conference on. :531-538 May, 2016
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Correlation
Sociology
Feature extraction
Computers
Algorithm design and analysis
Government
FP Tree
Fp Max
Correlation Analysis
CIP(Crime Intensity Point)
Data transformation Algorithm(DTA)
Classification
Prediction
KDD(Knowledge Discovery Process)
Language
Abstract
'Crime analysis plays a major part in crime prevention and safety of people in a country. The paper focuses on state based frequent crime pattern knowledge discovery and prevention. Our work concentrates on Finding frequent crimes state wise using FP Max a bottom up approach which uses linked lists for reduction of space complexity. The generated frequent crime sets of state will be undergone through knowledge discovery process. Correlation between the crime types is done to find the weightage factor of crime types to find crime intensity point. The crime intensity point of 29 states and 7 union territories are calculated according to the weightages derived from the correlation analysis. Later we classified the states as most dangerous, dangerous, moderate or safe based on their crime intensity point using Random forests classification technique. Prediction of crime intensity point for the state based on the responsible factors that contribute to a crime.