Introduction to Data Mining

Unit Structure

            
Introduction to Data Mining
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├── 1. Basic Concepts of Data Mining
│   ├── What is Data Mining?
│   ├── Knowledge Discovery in Databases (KDD) vs. Data Mining
│   ├── Data Mining Tools and Applications
│
├── 2. Data Mining Primitives
│   ├── Task-Relevant Data
│   ├── Mining Objectives
│   ├── Measures and Identification of Patterns
│
├── 3. Data Mining Query Languages
│   ├── Data Specification
│   ├── Specifying Kind of Knowledge
│   ├── Hierarchy Specification
│   ├── Pattern Presentation and Visualization Specification
│   ├── Data Mining Languages
│   └── Standardization of Data Mining
│
└── 4. Architectures of Data Mining Systems
            
        

• When we talk about Data Mining, we're basically talking about digging through huge amounts of data to find useful patterns, trends, or knowledge — sort of like finding gold in a mountain of rocks. This unit sets the stage by introducing you to the core ideas behind data mining and how it fits into the bigger picture of data analysis and business intelligence.
• We begin with the basic concepts, including what data mining really means, how it's different from (yet related to) KDD (Knowledge Discovery in Databases), and where it's used — from market research to fraud detection. Then we move into data mining primitives, which are like building blocks that help define what kind of patterns we want to find, what data is relevant, and how we measure our findings.
• Next, we explore data mining query languages, which allow us to express what kind of patterns we're interested in — whether it’s through specifying data, the kind of knowledge we want, how results should be visualized, or setting up hierarchies and structures. Finally, the unit wraps up with the architecture of data mining systems, explaining how everything works together behind the scenes — from data input to pattern output.