Data Mining as Knowledge Discovery — Steps

Data mining, viewed within the KDD (Knowledge Discovery in Databases) framework, involves the following iterative steps:

Raw Data
   │
   ▼
1. Data Cleaning ───────────▶ remove noise, inconsistent data
   │
   ▼
2. Data Integration ────────▶ combine multiple data sources
   │
   ▼
3. Data Selection ──────────▶ retrieve data relevant to the task
   │
   ▼
4. Data Transformation ─────▶ consolidate into forms suitable for mining
   │                           (aggregation, normalization)
   ▼
5. Data Mining ─────────────▶ apply algorithms to extract patterns
   │                           (classification, clustering, association)
   ▼
6. Pattern Evaluation ──────▶ identify truly interesting patterns
   │                           using interestingness measures
   ▼
7. Knowledge Presentation ──▶ visualize/present mined knowledge
   │                           (reports, graphs, rules)
   ▼
Useful Knowledge

Step-by-step explanation

1. Data Cleaning — removes noise and inconsistent/incomplete data.
2. Data Integration — merges data from multiple heterogeneous sources into one coherent store.
3. Data Selection — retrieves only the data relevant to the analysis task at hand.
4. Data Transformation — converts/consolidates data into forms appropriate for mining (e.g., aggregation, normalization, discretization).
5. Data Mining — the essential step where intelligent methods (classification, clustering, association rule mining, etc.) are applied to extract data patterns.
6. Pattern Evaluation — identifies truly interesting patterns representing knowledge, based on interestingness measures (support, confidence, novelty).
7. Knowledge Presentation — visualization and knowledge representation techniques present the mined knowledge to users in an understandable form.

Steps 1–4 together are often called preprocessing, and this whole pipeline is typically iterative — insights from later steps may send you back to refine earlier ones (e.g., pattern evaluation might reveal that different data selection is needed).