Data Reduction in Data Mining
Data mining is usually performed on very large datasets. However,
processing huge amounts of
data takes a lot of time and computational power. This makes analysis slow
and sometimes
impractical.
Data reduction helps solve this problem by reducing the size of data while
still keeping its
important information.
What is Data Reduction?
Data reduction is the process of converting a large dataset into a smaller
dataset that still
produces the same (or nearly the same) results when used for data
mining.
- It reduces data size
- Maintains data quality and meaning
- Improves processing speed
- Makes algorithms more efficient
Data can be reduced in two ways:
- Reducing number of rows (records)
- Reducing number of columns (attributes/features)
- Why Data Reduction is Important
- Faster data processing
- Lower storage requirements
- Easier to apply complex algorithms
- Reduces computational cost
Techniques of Data Reduction
1. Dimensionality Reduction
This technique reduces the number of attributes (columns) in a dataset
by removing
unnecessary or less important features.
Methods:
a) Wavelet Transform
- Converts original data into another form
- Keeps only important values (coefficients)
- Removes less important details
- Useful for compressed representation
b) Principal Component Analysis (PCA)
- Converts many attributes into a smaller number of new variables
- These new variables (components) still represent most of the data
- Helps in reducing complexity
c) Attribute Subset Selection
- Removes irrelevant and redundant attributes
- Keeps only useful features
- Maintains almost the same data accuracy
2. Numerosity Reduction
This technique reduces data volume by representing data in a
simpler form instead of storing
full data.
Types:
a) Parametric Methods
Store only model parameters instead of full data.
Regression
Finds relationship between variables
Example:
y=wx+b
Used to predict values
Log-Linear Model
Used for discrete data
Finds relationships between multiple attributes
b) Non-Parametric Methods
No assumptions about data model.
Histogram
Shows frequency distribution using bins
Simple way to summarize data
Clustering
Groups similar data into clusters
Each cluster represents many data points
Reduces data size effectively
Sampling
Selects a small subset from large data
Types of sampling:
- Simple random sampling (with/without replacement)
- Cluster sampling
- Stratified sampling (useful for skewed data)
- Data Cube Aggregation
- Summarizes data at different levels
Example:
Quarterly sales → Annual sales
Reduces data while keeping useful information
3. Data Cube Aggregation
Combines data into summarized form
Used in multidimensional analysis
Provides faster access to summarized data
4. Data Compression
Reduces storage space by encoding data.
Types:
a) Lossless Compression
Original data can be perfectly restored
Example: Run-Length Encoding
b) Lossy Compression
Some data is lost but still useful
Example: JPEG images
5. Discretization
Converts continuous data into intervals (ranges).
Example:
Age → Young, Middle, Old
Types:
1.Top-Down (Splitting)
Divide data step by step
2.Bottom-Up (Merging)
Combine smaller intervals into larger ones
Benefits of Data Reduction
- Saves storage space
- Reduces cost
- Improves processing speed
- Saves energy
- Makes data analysis easier
- Increases system efficiency
Data reduction is an important step in data mining. It helps in handling
large datasets efficiently
without losing important information. By using techniques like
dimensionality reduction,
sampling, and compression, we can make data mining faster and more
effective.
