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Intelligent Data Mining in Law Enforcement Analytics

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Cover of 'Intelligent Data Mining in Law Enforcement Analytics'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction to Artificial Networks and Law Enforcement Analytics
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    Chapter 2 Law Enforcement and Artificial Intelligence
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    Chapter 3 The General Philosophy of Artificial Adaptive Systems
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    Chapter 4 A Brief Introduction to Evolutionary Algorithms and the Genetic Doping Algorithm
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    Chapter 5 Artificial Adaptive Systems in Data Visualization: Proactive Data
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    Chapter 6 The Metropolitan Police Service Central Drug-Trafficking Database: Evidence of Need
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    Chapter 7 Supervised Artificial Neural Networks: Backpropagation Neural Networks
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    Chapter 8 Preprocessing Tools for Nonlinear Datasets
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    Chapter 9 Metaclassifiers
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    Chapter 10 Auto-Identification of a Drug Seller Utilizing a Specialized Supervised Neural Network
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    Chapter 11 Visualization and Clustering of Self-Organizing Maps
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    Chapter 12 Self-Organizing Maps: Identifying Nonlinear Relationships in Massive Drug Enforcement Databases
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    Chapter 13 Theory of Constraint Satisfaction Neural Networks
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    Chapter 14 Application of the Constraint Satisfaction Network
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    Chapter 15 Auto-Contractive Maps, H Function, and the Maximally Regular Graph: A New Methodology for Data Mining
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    Chapter 16 Analysis of a Complex Dataset Using the Combined MST and Auto-Contractive Map
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    Chapter 17 Auto-Contractive Maps and Minimal Spanning Tree: Organization of Complex Datasets on Criminal Behavior to Aid in the Deduction of Network Connectivity
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    Chapter 18 Data Mining Using Nonlinear Auto-Associative Artificial Neural Networks: The Arrestee Dataset
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    Chapter 19 Artificial Adaptive System for Parallel Querying of Multiple Databases
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Title
Intelligent Data Mining in Law Enforcement Analytics
Published by
Springer Netherlands, December 2014
DOI 10.1007/978-94-007-4914-6
ISBNs
978-9-40-074913-9, 978-9-40-079601-0, 978-9-40-074914-6
Editors

Buscema, Paolo Massimo, Tastle, William J.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 4%
Germany 1 4%
Unknown 21 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 13%
Student > Bachelor 3 13%
Other 2 9%
Professor 2 9%
Student > Master 2 9%
Other 5 22%
Unknown 6 26%
Readers by discipline Count As %
Business, Management and Accounting 5 22%
Computer Science 4 17%
Chemical Engineering 1 4%
Arts and Humanities 1 4%
Psychology 1 4%
Other 4 17%
Unknown 7 30%