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Data mining and predictive analysis : intelligence gathering and crime analysis /
Data 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 crim...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Butterworth-Heinemann,
2015.
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; 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?
- 1.5.6
- 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.
- 3.10
- 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.
- 4.8
- 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.
- 5.1
- 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.
- 5.5.3
- Misrepresentation, Fabrication, and Poor Recall.