Index_M


M

Machine-learning algorithms, 2, 11–13, 205–48

border case study, 210–12

C5.0, 12, 205, 206–7

CART, 12, 205, 206

CHAID, 12, 205, 206

criminal patterns, 219–21

decision trees, 207–8

defined, 205–6

fraud and, 267–70

functioning of, 206–7

government financial transaction detection case study, 213–19

IDS construction with, 322

military data extrapolation case study, 212

output, 12

rules generation, 206

rules predicting crime, 208–10

scenario, 12–13

SPSS Clementine, 26, 30, 179, 180, 181, 245–47

uses, 11

Machine-learning software, 233–48

ANGOSS KnowledgeSTUDIO, 233–37

Magaputer Polyanalyst, 238–41

Oracle9i Data Mining suite, 242–43

Prudsys DISCOVERER 2000, 241–42

Quadstone Decisionhouse, 243–44

SAS Sample, Explore, Modify, Model, Assess (SEMMA), 244

Teradata Warehouse Miner, 247–48

thinkAnalytics, 248

MATLAB Neural Net Toolbox, 198–99

Megaputer PolyAnalyst, 238–41

data access, 238

data import wizard, 239

data mining process support, 240

decision tree interface, 240

exploration engines, 238

modeling algorithms, 238

schematic decision tree, 241

Visual Rule Assistant, 239

See also Machine-learning software

Military data extrapolation case study, 212

Miscoding, 284–85

defined, 284

detection techniques, 284—85

MO, 284

See also Insurance fraud

Misuse detection, 310

Misuse IDSs, 318–19

defined, 318

NIDES, 318–19

See also Intrusion detection systems (IDSs)

MITRE intrusion detection case study, 313-18

Models

combining, 297–98

creating, 217–18

multiple, evaluating, 297

testing, 218

values, 39

Modus operandi (MO), 27

analysis, 190

attack, 307

bank fraud, 279

card-not-present fraud, 278

control, 307

credit-card fraud, 277

diversity, 35

excessive/inappropriate testing, 283

false claims, 282

financial crime, 277–79

free-text field, 36

identity brokers, 290

illegal billing, 282

information on, 31

intelligence, 303–5

intrusion, 302–9

loan default, 278

miscoding, 284

money laundering, 280

personal injury mills, 284

probing, 306

scanning, 305–6

stealth, 308–9

sub-classification, 187

text, 186

MO modeling case study, 179–95

building-type analysis, 189–90

business understanding, 181–83

CRISP-DM methodology, 180

data encoding, 187

data preparation, 185–86

discussion/conclusions, 192–93

introduction, 179–81

missing data, 186–87

MLP, 181

MO analysis, 190

model building, 190–91

offender behavior, 183–84

RBF, 181

SOM, 181

spectral analysis, 189

temporal analysis, 187–88

validation, 191–92

See also Neural networks

Money, as data, 276–77

Money laundering, 279–81

case study, 84–85

defined, 279

integration step, 280

layering step, 280

methods, 280

MO, 280

placement step, 280

Mueller, Robert S., 37–38

Multi-layer perceptrons (MLP) networks, 162–63, 179, 199

advantage, 162

defined, 162

inadequacies, 162

parallel, 163




Investigative Data Mining for Security and Criminal Detection
Investigative Data Mining for Security and Criminal Detection
ISBN: 0750676132
EAN: 2147483647
Year: 2005
Pages: 232
Authors: Jesus Mena

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