KDD – Knowledge Discovery in Databases
KDD is an interdisciplinary field that combines ideas from many areas such as Artificial Intelligence, Machine Learning, Pattern Recognition, Databases, Statistics, Expert Systems, and Data Visualization.
The main goal of KDD is to extract useful information from large databases. It uses Data Mining algorithms to analyze data and identify patterns that can be considered valuable knowledge.
KDD can be defined as a systematic and exploratory process used to analyze large datasets and build models from them. These models help in understanding the data, discovering hidden patterns, and making predictions.
Today, organizations generate huge amounts of data. Because of this, Knowledge Discovery and Data Mining have become very important for finding meaningful insights and supporting decision-making.
KDD Process
The process begins by understanding the problem and defining objectives, and ends with using the discovered knowledge in real applications.
The KDD process generally includes nine steps.
Steps in the KDD Process
1. Understanding the Application Domain
This is the first step in the KDD process.
In this stage, the people working on the project must understand:
- The problem to be solved
- The goals of the end user
- The environment where the system will be used
This step helps in deciding which data, methods, and algorithms should be used.
2. Selecting and Creating the Dataset
After defining the objectives, the next step is to select the data that will be used for analysis.
This includes:
- Identifying available data
- Collecting relevant data
- Combining data from different sources into one dataset
The quality of the dataset is very important because Data Mining learns patterns from the available data. If important attributes are missing, the results may not be accurate.
3. Data Preprocessing and Cleaning
In this step, the data is cleaned and prepared for analysis.
This may include:
- Handling missing values
- Removing noise and outliers
- Correcting inconsistent data
Sometimes statistical techniques or Data Mining algorithms are used to improve data quality.
For example, if some values are missing, prediction models can be used to estimate those values.
4. Data Transformation
In this step, the data is transformed into a suitable format for Data Mining.
Common techniques include:
- Feature selection – selecting important attributes
- Feature extraction – creating new useful attributes
- Data sampling – selecting a subset of records
- Discretization – converting numerical data into categories
This step is very important because the quality of transformation can affect the success of the entire KDD project.
5. Choosing the Type of Data Mining Task
Now we decide what type of Data Mining should be performed.
The two main goals are:
Prediction
Prediction is used to predict future values based on existing data.
Examples:
- Classification
- Regression
This is usually called Supervised Learning.
Description
Description focuses on finding patterns and relationships in data.
Examples:
- Clustering
- Association rules
- Data visualization
This is often called Unsupervised Learning.
6. Selecting the Data Mining Algorithm
After selecting the task, the next step is to choose the appropriate Data Mining algorithm.
Different algorithms have different advantages.
For example:
- Neural Networks – high prediction accuracy
- Decision Trees – easy to understand and interpret
Each algorithm also has parameters and training methods such as cross-validation for testing accuracy.
7. Applying the Data Mining Algorithm
In this stage, the selected algorithm is applied to the dataset.
The algorithm may be run multiple times while adjusting parameters to improve performance.
For example, in a decision tree, we may change parameters such as the minimum number of records in a node.
8. Evaluation and Interpretation
After obtaining the results, the discovered patterns must be evaluated and interpreted.
This step checks:
- Whether the results meet the original objectives
- Whether the model is accurate and useful
- Whether the results are easy to understand
The discovered knowledge is also documented for future use.
9. Using the Discovered Knowledge
The final step is to apply the discovered knowledge in real-world systems.
This may include:
- Improving business strategies
- Supporting decision-making
- Updating system processes
After implementation, the results are monitored and the KDD process may be repeated using new data.
