A survey of data mining techniques for analyzing crime patterns

Abstract

In recent years the data mining is data analyzing techniques that used to analyze crime data previously stored from various sources to find patterns and trends in crimes. In additional, it can be applied to increase efficiency in solving the crimes faster and also can be applied to automatically notify the crimes. However, there are many data mining techniques. In order to increase efficiency of crime detection, it is necessary to select the data mining techniques suitably. This paper reviews the literatures on various data mining applications, especially applications that applied to solve the crimes. Survey also throws light on research gaps and challenges of crime data mining. In additional to that, this paper provides insight about the data mining for finding the patterns and trends in crime to be used appropriately and to be a help for beginners in the research of crime data mining.

Crime analysis in India using data mining techniques

“…It basically has three layers: input layer, hidden layer and output layer. The neurons represent the relationship or connection between the three layers [11]. Google map marker clustering (GMAPI) is used in this research and it is very helpful in representing the crime prone regions of a country.…”

Predictive Modelling of Crime Dataset Using Data Mining

“…Data Mining [8] is the process of analysis to find trends, patterns, and knowledges. For this purpose, two factors are important viz.…”

“…Decision Trees [8] includes a root node, branches, and leaf nodes with each internal node denoting a test on an attribute, each branch denotes the outcome of a test, and each leaf node holds a class label. The Random Forest [21] considers multiple individual trees.…”

Crime Classification and Criminal Psychology Analysis using Data Mining

“…Ubon Thongsatapornwatana [10] analyses different data mining techniques used for crime detection. The author first talks about association rule mining which is an unsupervised learning method that used to find the hidden knowledge in unlabeled data and also used to discover the interesting co-occurrences of objects in large data sets.…”