The primary difference is that data mining is a specific step within the broader process of knowledge mining (often referred to as KDD - Knowledge Discovery in Databases).
According to the provided reference:
- Data mining is described as "the process of identifying patterns and extracting details about big data sets using intelligent methods."
- KDD (Knowledge Discovery in Databases), which can be equated to knowledge mining in this context, is called "a complex and iterative approach to knowledge extraction from big data."
Understanding Data Mining
Data mining focuses on the application of specific algorithms to find hidden patterns, correlations, anomalies, and structures within large datasets. Think of it as the engine that performs the analytical tasks. It uses various techniques from statistics, machine learning, and database systems.
Examples of what data mining techniques can reveal include:
- Identifying customer segments for targeted marketing.
- Predicting stock prices based on historical data.
- Detecting fraudulent transactions.
- Finding associations between products purchased together (e.g., market basket analysis).
Understanding Knowledge Mining (KDD)
Knowledge mining, or KDD, is a more comprehensive process that involves several stages designed to transform raw data into understandable and useful knowledge. Data mining is just one, albeit crucial, step within this larger framework.
The KDD process typically involves the following iterative steps:
- Selection: Defining the target data for the discovery process.
- Preprocessing: Cleaning and preparing the data (handling missing values, noise, etc.).
- Transformation: Transforming the data into appropriate formats for mining (e.g., aggregation, feature selection).
- Data Mining: Applying algorithms to find patterns and models in the data (this is where data mining fits in).
- Evaluation/Interpretation: Evaluating the discovered patterns and interpreting them as knowledge. This step involves human understanding and validation.
- Deployment: Utilizing the discovered knowledge.
Key Differences Summarized
Here's a simple comparison based on the provided reference and general understanding:
| Feature | Data Mining | Knowledge Mining (KDD) |
|---|---|---|
| Scope | Specific step focusing on pattern discovery | Broad, iterative process of knowledge extraction |
| Goal | Identify patterns & extract details | Extract knowledge from data |
| Methods | Uses intelligent methods (algorithms) | Integrates multiple stages & methods |
| Relationship | Part of Knowledge Mining | Encompasses Data Mining |
In essence, knowledge mining is the overarching goal and process of turning data into valuable insights and actionable knowledge, while data mining is the specific technique used within that process to identify the underlying patterns. You perform data mining to achieve knowledge mining.
