Pattern Analysis from Crime Data

Ramesh kumar K

ISBN 10: 6209361390 / ISBN 13: 9786209361395

Published by LAP LAMBERT Academic Publishing, 2025

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - In recent days, the association analysis is put into practice with legal datasets. The main aim of this research work is to mine association rule from various theft cases collected from different sources within the jurisdiction of State of Tamil Nadu.First, in this thesis it is proposed an innovative Theft Pattern Mining algorithm to mine the frequent item set. The proposed data structure is applied in Theft Pattern Mining algorithm. The performance of the proposed algorithm is compared with the existing Frequent Pattern Mining (FPM) algorithms. The proposed algorithm is comparatively analyzed with the existing U-Apriori, FP-growth and UF-growth algorithms. The performance of the proposed algorithm is studied by using synthetic dataset like T40I10D100K, real dataset like Mushroom, Gazella and the proposed Tamil Nadu Theft Crime (TTC) dataset with special reference to State of Tamil Nadu.

Seller Inventory # 9786209361395

Bibliographic Details

• Title: Pattern Analysis from Crime Data
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2025
• Language: English
• ISBN 10: 6209361390
• ISBN 13: 9786209361395
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 15D millimeters 220 millimeters width by 150 millimeters height by 15 millimeters depth
• Weight: 369 grams

About this title

Synopsis

In recent days, the association analysis is put into practice with legal datasets. The main aim of this research work is to mine association rule from various theft cases collected from different sources within the jurisdiction of State of Tamil Nadu.First, in this thesis it is proposed an innovative Theft Pattern Mining algorithm to mine the frequent item set. The proposed data structure is applied in Theft Pattern Mining algorithm. The performance of the proposed algorithm is compared with the existing Frequent Pattern Mining (FPM) algorithms. The proposed algorithm is comparatively analyzed with the existing U-Apriori, FP-growth and UF-growth algorithms. The performance of the proposed algorithm is studied by using synthetic dataset like T40I10D100K, real dataset like Mushroom, Gazella and the proposed Tamil Nadu Theft Crime (TTC) dataset with special reference to State of Tamil Nadu.