Index_C

C

Calibrating crime, 22–24

Calls for service (CFS), 47

Card-not-present fraud, 277–78

Case Notebook, 90

Case studies

bio-surveillance, 117–20

border machine learning, 210–12

Chicago Internal Affairs, 167–68

ChoicePoint, 66–68

chromatogram retrieval system, 172–77

clustering burglars, 24–37

death claims, 287–88

drug trafficking, 81–82

government financial transaction detection, 213–19

intrusion detection, 313–18

military data extrapolation, 212

MO modeling, 179–95

money laundering, 84–85

sexual assault offender behavior modeling, 348–62

wireless fraud, 78–79

CATCH, 364–75

abstract, 365

analysis tools, 366

ANNs, 365, 366

clustering algorithm, 366–67

clustering tools, 365

conclusion, 374

crime data vectors, 370

crime similarity, viewing, 371

crime type comparison, 372

database mining, 367–70

database visualization, 370–73

defined, 365

evaluation, 373–74

introduction, 365–66

order and description of crimes, 369

potential, 374

SOMs, 366, 367

SQL query generation, 369

starmap of crimes, 368

tool types, 367

versions, 366

Center of Excellence in Document Analysis and Recognition (CEDAR) project, 363–64

defined, 363

information on, 364

Charts

clarity, 92–93

confirmed/unconfirmed lines, 91

high volume data, 95

legend, 94

link types, 93

organizations inside boxes, 91–92

telephone toll analysis, 94

See also Analyst's Notebook

Chicago Internal Affairs case study, 167–68

Chi-square automatic interaction detection (CHAID), 12, 205

design, 206

operation, 12

See also Machine-learning algorithms

ChoicePoint case study, 66–68

AutoTrack Wireless, 66

CORE service, 68

defined, 66

National Comprehensive Report, 67–68

notification, 68

SQL Direct, 66–67

Chromatogram retrieval system case study, 172–77

Clairvoyance, 142–43

applications, 143

defined, 142

functions, 142–43

See also Text mining tools

Classification, 159

Classification and regression trees (CART), 205, 224–27

accuracy, 225

binary trees, 226

DBMS/COPY, 225

defined, 224

design, 206

IF/THEN rule generation, 224

interface, 225

rules generation, 227

rules sample, 228

See also Decision tree tools; machine-learning algorithms

Classifier version 5 (C5.0), 12, 205

classifiers, 224

defined, 206–7, 224

See also Machine-learning algorithms

ClearForest, 143–44

defined, 143

dynamic view of relationships, 144

output, 144

taxonomy graphical view, 143

See also Text mining tools

Clementine, 26, 30, 179, 245–47

application templates (CATs), 245–47

COP CAT, 245–46

defined, 245

government fraud CAT, 246

police computer systems interface, 181

SOM option, 31

See also Machine-learning software; SPSS

Clustering, 159–60

border smugglers with SOM, 169–72

fraud, 262–63

Clustering burglars case study, 24–37

application construction, 30–31

application task, 26–27

conclusions, 36

data selection, cleaning, coding, 27–30

discussion, 34–36

findings, 31

introduction, 25–26

room for improvement, 34–35

validation process, 31–33

Clusters

analysis, 31–32

crime, 346–47

dendrograms for, 35

COGNOS 4Though, 197

Cognos Scenario, 227–28

Combined DNA Index System (CODIS), 52

Computer-aided dispatch (CAD) systems, 343

Computer Aided Tracking and Characterization of Homicides and Sexual Assaults. See CATCH

Conditional rules, 21

Consolidation, 78

Cookies, 57–58

anatomy, 58

defined, 57

example, 57–58

See also Internet data

Copernic, 144–45

Credit-card fraud, 250–51

detection technique, 277

MO, 277

Credit data, 46–47

Crime clusters, 346–47

Crime detection

data balancing, 296

data mining methodology, 293–96

data rotation, 297

data splitting, 296–97

deployment and monitoring, 298–99

false positive measurements, 298

mechanisms, 296–99

model combining, 297–98

multiple model evaluation, 297

random sampling, 296

Crime Link, 97–98

defined, 97

diagram generation, 98, 99

two-dimensional association matrix, 97–98

See also Link analysis tools

Crime maps, 343–45

defined, 343–44

illustrated, 345

interactive, 345–46

persuasive nature of, 344–45

Crime(s)

bogus official, 25, 26, 27

calibrating, 22–24

by day of week, 188

detecting, through data mining, 220–21

detection methodology, 293–96

digital, 219–20

financial, 277–78

identity, 291–92

insurance, 281–87

mapping, 343–78, 370

probability of, 23

property, 18

rate, computing, 16–18

reports, 27

similarity of, 371

targets of, 18

telecommunications, 288–90

by time of day, 188

type comparison, 372

Crime Workbench, 98–100

Action Management module, 98

database searches, 99

defined, 98

Link Management module, 99

Web, 99–100

See also Link analysis tools

Criminal analysis

comparison crime rates, 16–18

crime pattern breakdown, 16

data mining and, 15–19

environment and, 16

goal, 16

Criminal data, 47–55

aggregate statistics, 49

CFFS, 47

CFS, 47

CJIS WAN, 52

CODIS, 52

DRUGX, 53

FinCEN, 53

GREAT, 54

IDENT-INS, 53

JABS, 53

LEO, 53

NCIC, 51–52

NCVS, 48

NDPIX, 53

NIBIN, 52

NIBRS, 47, 48–49

NICS, 53

NIPC, 53

NLETS, 50–51

RISS, 53

SWBS ADIS, 53

TECS, 53

UCR/NIBRS, 53

Web sites, 54–55

WIN, 53

See also Data

Criminal Justice Information Services Wide Area Network (CJIS WAN), 52

Criminal profiling. See Profiling

CRISP-DM, 180, 293–96, 349

concept, 293–94

data preparation, 294–95

data understanding, 294

defined, 293

deployment, 295–96

diagram arrows, 293

evaluation, 295

investigation objective understanding, 294

modeling, 295

objective, 294

phases, 293

processes, 294–96

Cron, 308

CRoss-Industry Standard Process-Data Mining. See CRISP-DM

Customer-relationship management (CRM), 41

Cybercrimes, 301–2

CyberSource, 271