Z - CRIME: A data mining tool for the detection of suspicious criminal activities based on decision tree

Abstract

Data mining is the extraction of knowledge from large databases. One of the popular data mining techniques is Classification in which different objects are classified into different classes depending on the common properties among them. Decision Trees are widely used in Classification. This paper proposes a tool which applies an enhanced Decision Tree Algorithm to detect the suspicious e-mails about the criminal activities. An improved ID3 Algorithm with enhanced feature selection method and attribute-importance factor is applied to generate a better and faster Decision Tree. The objective is to detect the suspicious criminal activities and minimize them. That's why the tool is named as "Z-Crime" depicting the "Zero Crime" in the society. This paper aims at highlighting the importance of data mining technology to design proactive application to detect the suspicious criminal activities.

A C4.5 algorithm for english emotional classification

“…In many studies related to the decision tree for sentiment classification (opinion analysis, semantic classification) in the world and in (Mita 2011;Taboada et al 2008;Nizamani et al 2012;Wan et al 2015;Winkler et al 2015, 20, 21;Vinodhini and Chandrasekaran 2013, 23, 24;Opinion 2015;Prasad et al 2016, 27;Mugdha;Sharma 2014;Park et al 2003;Loh and Mauricio 2003), there is not any CArelated research for semantic classification, which is similar to our work.…”

“…There are many researches related to a decision tree for sentiment classification in (Mita 2011;Taboada et al 2008;Nizamani et al 2012;Wan et al 2015;Winkler et al 2015;Psomakelis et al 2015;Vinodhini and Chandrasekaran 2013, 23;Mandal et al 2014;Kaur et al 2015;Prasad et al 2016;Pong-Inwong et al 2014;Mugdha;Sharma 2014;Park et al 2003;Loh and Mauricio 2003). Automatic Text Classification (Mita 2011) is a semi-supervised machine learning task that automatically assigns a given document to a set of pre-defined categories based on its textual content and extracted features.…”

KNN Classifier and Naive Bayse Classifier for Crime Prediction in San Francisco Context

“…Sharma presents a paper that employs decision tree-based classification approach to detect criminal activities [11]. In this study he found that an advanced decision tree classifier and feature selection method can provide good classification result for suspicious e-mail detection.…”

Using Mapreduce Techniques to Predict and Examine Crime Pattern

“…The elements of an article are categorized as for whether it is an "offense" or "non-offense" article. An improved "ID3 calculation", an enhanced "Include Preference Technique", a credit important change to reserve message categorized as either probable "suspicious" or "non-suspicious" message was proposed by Sharma, reference [13]. The use of order approach referred to as "sender-notoriety calculation" with the collective customer critique database helps produce the "Framework of Marketing or Newsletter Sender Reputation System" (FMNSRS) as proposed by [14].…”