LEADER 07583nam 2200745Ia 450 001 9910974281703321 005 20260112191116.0 010 $a9786610636501 010 $a9781280636509 010 $a1280636505 010 $a9780080464626 010 $a0080464629 024 3 $z9780750677967 035 $a(CKB)1000000000349942 035 $a(EBL)274667 035 $a(OCoLC)271426919 035 $a(SSID)ssj0000135014 035 $a(PQKBManifestationID)11144189 035 $a(PQKBTitleCode)TC0000135014 035 $a(PQKBWorkID)10058217 035 $a(PQKB)11115322 035 $a(Au-PeEL)EBL274667 035 $a(CaPaEBR)ebr10150576 035 $a(CaONFJC)MIL63650 035 $a(CaSebORM)9780750677967 035 $a(MiAaPQ)EBC274667 035 $a(OCoLC)824876093 035 $a(OCoLC)ocn824876093 035 $a(EXLCZ)991000000000349942 100 $a20060414d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData mining and predictive analysis $eintelligence gathering and crime analysis /$fColleen McCue 205 $a1st ed. 210 $aAmsterdam ;$aBoston $cButterworth-Heinemann$dc2007 215 $aXXVII, 393 p.;$d24 cm 311 08$a9780750677967 311 08$a0750677961 320 $aIncludes bibliographical references and index. 327 $aFront Cover -- Title page -- Copyright Page -- Table of Contents -- Foreword -- Preface -- Introduction -- How To Use This Book -- Bibliography -- Introductory Section -- 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 -- 1.6 Overfitting the Model -- 1.7 Generalizability versus Accuracy -- 1.8 Input/Output -- 1.9 Bibliography -- 2 Domain Expertise -- 2.1 Domain Expertise -- 2.2 Domain Expertise for Analysts -- 2.3 Compromise -- 2.4 Analyze Your Own Data -- 2.5 Bibliography -- 3 Data Mining -- 3.1 Discovery and Prediction -- 3.2 Confirmation and Discovery -- 3.3 Surprise -- 3.4 Characterization -- 3.5 "Volume Challenge" -- 3.6 Exploratory Graphics and Data Exploration -- 3.7 Link Analysis -- 3.8 Nonobvious Relationship Analysis (NORA) -- 3.9 Text Mining -- 3.10 Future Trends -- 3.11 Bibliography -- Methods -- 4 Process Models for Data Mining and Analysis -- 4.1 CIA Intelligence Process -- 4.2 CRISP-DM -- 4.3 Actionable Mining and Predictive Analysis for Public Safety and Security -- 4.4 Bibliography -- 5 Data -- 5.1 Getting Started -- 5.2 Types of Data -- 5.3 Data -- 5.4 Types of Data Resources -- 5.5 Data Challenges -- 5.6 How Do We Overcome These Potential Barriers? -- 5.7 Duplication -- 5.8 Merging Data Resources -- 5.9 Public Health Data -- 5.10 Weather and Crime Data -- 5.11 Bibliography -- 6 Operationally Relevant Preprocessing -- 6.1 Operationally Relevant Recoding -- 6.2 Trinity Sight -- 6.3 Duplication -- 6.4 Data Imputation -- 6.5 Telephone Data -- 6.6 Conference Call Example -- 6.7 Internet Data -- 6.8 Operationally Relevant Variable Selection -- 6.9 Bibliography -- 7 Predictive Analytics -- 7.1 How to Select a Modeling Algorithm, Part I -- 7.2 Generalizability versus Accuracy -- 7.3 Link Analysis. 327 $a7.4 Supervised versus Unsupervised Learning Techniques -- 7.5 Discriminant Analysis -- 7.6 Unsupervised Learning Algorithms -- 7.7 Neural Networks -- 7.8 Kohonan Network Models -- 7.9 How to Select a Modeling Algorithm, Part II -- 7.10 Combining Algorithms -- 7.11 Anomaly Detection -- 7.12 Internal Norms -- 7.13 Defining "Normal" -- 7.14 Deviations from Normal Patterns -- 7.15 Deviations from Normal Behavior -- 7.16 Warning! Screening versus Diagnostic -- 7.17 A Perfect World Scenario -- 7.18 Tools of the Trade -- 7.19 General Considerations and Some Expert Options -- 7.20 Variable Entry -- 7.21 Prior Probabilities -- 7.22 Costs -- 7.23 Bibliography -- 8 Public Safety-Specific Evaluation -- 8.1 Outcome Measures -- 8.2 Think Big -- 8.3 Training and Test Samples -- 8.4 Evaluating the Model -- 8.5 Updating or Refreshing the Model -- 8.6 Caveat Emptor -- 8.7 Bibliography -- 9 Operationally Actionable Output -- 9.1 Actionable Output -- Applications -- 10 Normal Crime -- 10.1 Knowing Normal -- 10.2 "Normal" Criminal Behavior -- 10.3 Get to Know "Normal" Crime Trends and Patterns -- 10.4 Staged Crime -- 10.5 Bibliography -- 11 Behavioral Analysis of Violent Crime -- 11.1 Case-Based Reasoning -- 11.2 Homicide -- 11.3 Strategic Characterization -- 11.4 Automated Motive Determination -- 11.5 Drug-Related Violence -- 11.6 Aggravated Assault -- 11.7 Sexual Assault -- 11.8 Victimology -- 11.9 Moving from Investigation to Prevention -- 11.10 Bibliography -- 12 Risk and Threat Assessment -- 12.1 Risk-Based Deployment -- 12.2 Experts versus Expert Systems -- 12.3 "Normal" Crime -- 12.4 Surveillance Detection -- 12.5 Strategic Characterization -- 12.6 Vulnerable Locations -- 12.7 Schools -- 12.8 Data -- 12.9 Accuracy versus Generalizability -- 12.10 "Cost" Analysis -- 12.11 Evaluation -- 12.12 Output -- 12.13 Novel Approaches to Risk and Threat Assessment. 327 $a12.14 Bibliography -- Case Examples -- 13 Deployment -- 13.1 Patrol Services -- 13.2 Structuring Patrol Deployment -- 13.3 Data -- 13.4 How To -- 13.5 Tactical Deployment -- 13.6 Risk-Based Deployment Overview -- 13.7 Operationally Actionable Output -- 13.8 Risk-Based Deployment Case Studies -- 13.9 Bibliography -- 14 Surveillance Detection -- 14.1 Surveillance Detection and Other Suspicious Situations -- 14.2 Natural Surveillance -- 14.3 Location, Location, Location -- 14.4 More Complex Surveillance Detection -- 14.5 Internet Surveillance Detection -- 14.6 How To -- 14.7 Summary -- 14.8 Bibliography -- Advanced Concepts and Future Trends -- 15 Advanced Topics -- 15.1 Intrusion Detection -- 15.2 Identify Theft -- 15.3 Syndromic Surveillance -- 15.4 Data Collection, Fusion and Preprocessing -- 15.5 Text Mining -- 15.6 Fraud Detection -- 15.7 Consensus Opinions -- 15.8 Expert Options -- 15.9 Bibliography -- 16 Future Trends -- 16.1 Text Mining -- 16.2 Fusion Centers -- 16.3 "Functional" Interoperability -- 16.4 "Virtual" Warehouses -- 16.5 Domain-Specific Tools -- 16.6 Closing Thoughts -- 16.7 Bibliography -- Index. 330 $aIt 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 executi 517 3 $aIntelligence gathering and crime analysis 606 $aCrime analysis 606 $aData mining 606 $aLaw enforcement$xData processing 606 $aCriminal behavior, Prediction of 615 0$aCrime analysis. 615 0$aData mining. 615 0$aLaw enforcement$xData processing. 615 0$aCriminal behavior, Prediction of. 676 $a363.25/6 686 $a54.64$2bcl 700 $aMcCue$b Colleen$01888455 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910974281703321 996 $aData mining and predictive analysis$94527360 997 $aUNINA