A new efficient SIF-based FCIL (SIF–FCIL) mining algorithm in predicting the crime locations

Abstract

In our innovative crime location forecast method, at the outset, the crime features are mined from the crime database and used for performing the adaptive mutation-based artificial bee colony (AMABC) algorithm, in which the database attributes and crime values are bunched together. Subsequently, the frequent closed itemsets lattice (FCIL) is built by the rules support factor values, and from this the frequent rules are extracted. In the course of the FCIL creation, the clustered attributes values are processed like a sliding window. In accordance with the frequent rules, the related crime locations are created. Thus, our proposed sliding with itemsets factor-based FCIL proposed technique is endowed with the superb skill of fruitfully forecasting the locations by means of AMABC and FCIL methods. In our innovative approach, we apply an UCI Machine Learning Repository-Communities and Crime Data Set for the offence investigation. The novel method is analysed and contrasted with the modern mining algorithms such as Apriori, Eclat and conservative FCIL.

Computing the Probability on Socio Economic Factors to Predict the Crime Locations by Means of Joint Probability Based AMABC-FCIL

“…While mining the locations, the technique requires more computation time and scanning time, since the number of itemsets is large. So to avoid these negative aspects, AMABC is combined with modified FCIL to mine the location in less time [16]. Here we have mined only closed itemsets rather than the frequent itemsets.…”

Tracing the Emotional Roadmap of Depressive Users on Social Media Through Sequential Pattern Mining

“…Sequence pattern mining approaches can provide longitudinal analysis and predictions in different contexts and applications, e.g., stroke prediction [19], crime location prediction [20], prediction of cerebellar ataxia based on body activity detected by sensors [21], opinion analysis on Twitter (e.g., during the American pre-election campaign) [22], stress detection using smartphones [23]. Temporal analysis of feelings in social networks has to be subject of a study aiming to extract characteristics of feelings, mainly in specific contexts, such as from students, regions, workers, and visual analysis over time, without considering specific approaches of sequence pattern mining [24]- [29].…”

An approach to the sequential evaluation of emotional behaviors of depressive users on social networks in groups and individually

“…Sequence pattern mining approaches can provide longitudinal analysis and predictions in different contexts and applications, e.g., stroke prediction (Nasingkhun;Songram, 2018), crime location prediction (SUJATHA; EZHILMARAN, 2016), prediction of cerebellar ataxia based on body activity detected by sensors (Jin et al, 2020), opinion analysis on Twitter (e.g., during the American pre-election campaign) (Diaz-Garcia; Ruiz; Martin-Bautista, 2020), stress detection using smartphones (Alibasa;Calvo;Yacef, 2019). Temporal analysis of feelings in social networks has to be subject of a study aiming to extract characteristics of feelings, mainly in specific contexts, such as from students, regions, workers, and visual analysis over time, without considering specific approaches of sequence pattern mining (CHOUDHURY et al, 2013b;SEABROOK et al, 2018;CHEN et al, 2018;YAO et al, 2020;PRIMACK et al, 2021).…”