Data Mining and Predictive Analysis
Intelligence Gathering and Crime Analysis

Cuts through technical language to provide an ideal primer for those looking to use data mining in crime and intelligence analysis.

Colleen McCue (Author)

9780128002292, Elsevier Science

Paperback, published 6 January 2015

422 pages, Approx. 100 illustrations
23.4 x 19 x 2.7 cm, 0.97 kg

"...presents a comprehensive overview of the field of predictive analysis as it is primarily applied to crime analysis, where it is an established law enforcement intelligence practice with great success…" --Journal of Counterterrorism and Homeland Security International

Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, 2nd Edition, describes clearly and simply how crime clusters and other intelligence can be used to deploy security resources most effectively. Rather than being reactive, security agencies can anticipate and prevent crime through the appropriate application of data mining and the use of standard computer programs. Data Mining and Predictive Analysis offers a clear, practical starting point for professionals who need to use data mining in homeland security, security analysis, and operational law enforcement settings.This revised text highlights new and emerging technology, discusses the importance of analytic context for ensuring successful implementation of advanced analytics in the operational setting, and covers new analytic service delivery models that increase ease of use and access to high-end technology and analytic capabilities. The use of predictive analytics in intelligence and security analysis enables the development of meaningful, information based tactics, strategy, and policy decisions in the operational public safety and security environment.

Introductory Section Chapter 1: Basics Chapter 2: Domain Expertise Chapter 3: Data mining Methods Chapter 4: Process Models for Data Mining and Analysis Chapter 5: Data Chapter 6: Operationally-relevant preprocessing Chapter 7: Identification, Characterization and Modeling Chapter 8: Evaluation Chapter 9: Operationally-Actionable Output Applications Chapter 10: “Normal? Crime Chapter 11: Behavioral Analysis of Violent Crime Chapter 12: Risk and Threat Assessment Case Examples Chapter 13: Deployment Chapter 14: Surveillance Detection Advanced Concepts and Future Trends Chapter 15: Advanced Concepts in Data Mining Chapter 16: Future Trends

Subject Areas: Data mining [UNF], Probability & statistics [PBT], Forensic science [JKVF1]