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020 _a0128004088
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020 _z9780128002292
020 _z0128002298
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035 _a(OCoLC)896850431
_z(OCoLC)899941963
035 _a(OCoLC)ocn896850431
037 _a6AA69EE0-51C8-4AEE-80C5-D9D414E69714
_bOverDrive, Inc.
_nhttp://www.overdrive.com
039 9 _y12-21-2016
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082 0 4 _a363.256
_223
100 1 _aMcCue, Colleen,
_eauthor.
245 1 0 _aData mining and predictive analysis
_h[electronic resource] :
_bintelligence gathering and crime analysis /
_cColleen McCue.
250 _aSecond edition.
264 1 _aAmsterdam :
_bButterworth-Heinemann,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
336 _astill image
_bsti
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
504 _aIncludes bibliographical references and index.
505 0 _aCover; Title Page; Copyright Page; Dedication; Contents; Foreword; Preface; Digital Assets; For the instructor; Introduction; Skill set; How to use this book; Part 1 -- Introductory Section; Chapter 1 -- Basics; 1.1 -- Basic statistics; 1.2 -- Inferential versus Descriptive Statistics and Data Mining; 1.3 -- Population versus Samples; 1.4 -- Modeling; 1.5 -- Errors; 1.5.1 -- Infrequent Events; 1.5.2 --'Black Swans'; 1.5.3 -- Identifying Appropriate Comparison Groups and Establishing the Denominator; 1.5.4 -- Remember the Baseline; 1.5.5 -- Where Did Your Data Come From?
505 8 _a1.5.6 -- Magnified or Obscured Effects1.5.7 -- Outliers; 1.6 -- Overfitting the Model; 1.7 -- Generalizability versus Accuracy; 1.8 -- Input/Output; Chapter 2 -- Domain Expertise; 2.1 -- Domain expertise; 2.2 -- Domain Expertise for Analysts; 2.3 -- The Integrated Model; Chapter 3 -- Data Mining and Predictive Analytics; 3.1 -- Discovery and Prediction; 3.2 -- Confirmation and discovery; 3.3 -- Surprise; 3.4 -- Characterization; 3.5 --'Volume Challenge'16; 3.6 -- Exploratory Graphics and Data Exploration; 3.7 -- Link Analysis21; 3.8 -- Non-Obvious Relationship Analysis (NORA)24; 3.9 -- Text Mining.
505 8 _a3.10 -- Closing ThoughtsPart 2 -- Methods; Chapter 4 -- Process Models for Data Mining and Predictive Analysis; 4.1 -- CIA Intelligence Process7; 4.1.1 -- Requirements; 4.1.2 -- Collection; 4.1.3 -- Processing and Exploitation; 4.1.4 -- Analysis and Production; 4.1.5 -- Dissemination; 4.1.6 -- Feedback; 4.1.7 -- Summary; 4.2 -- Cross-industry Standard Process for Data Mining; 4.2.1 -- Business Understanding; 4.2.2 -- Data Understanding; 4.2.3 -- Data Preparation; 4.2.4 -- Modeling; 4.2.5 -- Evaluation; 4.2.6 -- Deployment; 4.7.1 -- Summary.
505 8 _a4.8 -- Actionable Mining and Predictive Analysis for Public Safety and Security4.8.1 -- Question or Challenge; 4.8.2 -- Data Collection and Fusion; 4.8.3 -- Operationally Relevant Preprocessing; 4.8.3.1 -- Recoding; 4.8.3.2 -- Variable Selection; 4.8.3.3 -- Operational Value; 4.8.3.4 -- Availability and Timeliness; 4.8.4 -- Identification, Characterization, and Modeling; 4.8.5 -- Public Safety and Security-Specific Evaluation; 4.8.6 -- Operationally Actionable Output; 4.8.7 -- Additional Considerations; 4.8.7.1 -- Privacy; 4.8.7.2 -- Security; 4.8.7.3 -- Other Hazards; 4.8.8 -- Summary; Chapter 5 -- Data.
505 8 _a5.1 -- Getting started5.2 -- Types of data; 5.3 -- Data5; 5.3.1 -- Big Data; 5.3.1.1 -- Volume; 5.3.1.2 -- Velocity; 5.3.1.3 -- Variety; 5.4 -- Types of Data Resources; 5.4.1 -- Data Sources; 5.4.1.1 -- Records Management Systems; 5.4.1.2 -- Calls for Service; 5.4.2 -- Relational Data; 5.4.3 -- Revisiting the INTs; 5.4.4 -- Geoint19; 5.4.4.1 -- Physical Geography; 5.4.4.2 -- Human Geography; 5.4.5 -- Ad Hoc, Self-Generated, and Other Specialized Databases22; 5.4.6 -- Nontraditional Sources (e.g., Weather); 5.5 -- Data Challenges; 5.5.1 -- Reliability and Validity; 5.5.2 -- Data Entry Errors.
505 8 _a5.5.3 -- Misrepresentation, Fabrication, and Poor Recall.
520 _aData 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.
588 0 _aCIP data; resource not viewed.
590 _aElsevier
_bScienceDirect All Books
650 0 _aCrime analysis.
650 0 _aData mining.
650 0 _aLaw enforcement
_xData processing.
650 0 _aCriminal behavior, Prediction of.
650 7 _aBUSINESS & ECONOMICS / Infrastructure
_2bisacsh
650 7 _aSOCIAL SCIENCE / General
_2bisacsh
650 7 _aCrime analysis.
_2fast
_0(OCoLC)fst00883022
650 7 _aCriminal behavior, Prediction of.
_2fast
_0(OCoLC)fst00883179
650 7 _aData mining.
_2fast
_0(OCoLC)fst00887946
650 7 _aLaw enforcement
_xData processing.
_2fast
_0(OCoLC)fst00993947
650 4 _aCrime analysis.
650 4 _aCriminal behavior, Prediction of.
650 4 _aData mining in law enforcement.
650 4 _aLaw enforcement
_xData processing.
655 4 _aElectronic books.
776 0 8 _iPrint version:
_aMcCue, Colleen.
_tData Mining and Predictive Analysis : Intelligence Gathering and Crime Analysis.
_dBurlington : Elsevier Science, ò015
_z9780128002292
856 4 0 _uhttps://eresourcesptsl.ukm.remotexs.co/user/login?url=http://www.sciencedirect.com/science/book/9780128002292
907 _a.b16399882
_b2022-11-01
_c2019-11-12
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998 _ae
_b2016-08-12
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