rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Data mining and predictive analysis
    sub title: intelligence gathering and crime analysis
  title info:
    title: Referex
  name:
    name part: McCue, Colleen.
    role:
      role term: creator
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: ne
    date issued: 2007
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: remote
    extent: xxxi, 332 pages : illustrations, maps ; 24 cm.
  abstract: It is now possible to predict the future when it comes to crime. In Data Mining and Predictive Analysis, Dr. Colleen McCue describes not only the possibilities for data mining to assist law enforcement professionals, but also provides real-world examples showing how data mining has identified crime trends, anticipated community hot-spots, and refined resource deployment decisions. In this book Dr. McCue describes her use of "off the shelf" software to graphically depict crime trends and to predict where future crimes are likely to occur. Armed with this data, law enforcement executives can develop "risk-based deployment strategies," that allow them to make informed and cost-efficient staffing decisions based on the likelihood of specific criminal activity. Knowledge of advanced statistics is not a prerequisite for using Data Mining and Predictive Analysis. The book is a starting point for those thinking about using data mining in a law enforcement setting. It provides terminology, concepts, practical application of these concepts, and examples to highlight specific techniques and approaches in crime and intelligence analysis, which law enforcement and intelligence professionals can tailor to their own unique situation and responsibilities. * Serves as a valuable reference tool for both the student and the law enforcement professional * Contains practical information used in real-life law enforcement situations * Approach is very user-friendly, conveying sophisticated analyses in practical terms.
  table of contents: 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.
  note: Colleen McCue.
  note: Electronic resource.
  note: Electronic reproduction. Amsterdam : Elsevier Science & Technology, 2007. Mode of access: World Wide Web. System requirements: Web browser. Title from title screen (viewed on July 25, 2007). Access may be restricted to users at subscribing institutions.
  note: Includes bibliographical references and index.
  subject:
    topic: Crime analysis
  subject:
    topic: Data mining
  subject:
    topic: Law enforcement
    topic: Data processing
  subject:
    topic: Criminal behavior, Prediction of
  classification: HV7936.C88 M37 2007eb
  classification: 363.25/6
  related item:
    identifier: (DLC) 2006040568
    identifier: (OCoLC)67392931
  identifier: 9780750677967
  identifier: 0750677961
  identifier: 103995:104016 Elsevier Science & Technology
  identifier: http://proxy.library.tamu.edu/login
  identifier: http://www.loc.gov/catdir/enhancements/fy0646/2006040568-d.html
  related item:
    location:
      url: http://www.loc.gov/catdir/enhancements/fy0646/2006040568-d.html
  record info:
    record content source: OPELS
    record creation date: 070802
    record change date: 20260609193606.8
    record identifier: in00002425091