Data Mining vs Machine Learning
Data Mining and Machine Learning are closely related fields used to analyze
large amounts of data. Both aim to discover useful information and patterns
from data. However, their goals and methods are different.
Data Mining focuses on finding patterns and useful information from large
datasets. It is commonly used in business analytics and is based on concepts
from databases and statistics.
Machine Learning focuses on creating algorithms that allow computers to learn
from data automatically. These algorithms improve their performance over time
and can be used to predict future outcomes.
Although data mining and machine learning influence each other and share many
techniques, they serve different purposes.
What is Data Mining?
Data Mining is the process of extracting useful information or previously
unknown patterns from large datasets. The term “mining” means searching for
valuable information within a large amount of data.
Data Mining is also known as the Knowledge Discovery in Databases (KDD)
process. The term Knowledge Discovery in Databases was introduced by Gregory
Piatetsky-Shapiro in 1989, and the term Data Mining became popular in the
database community around 1990
Data mining is used to analyze large datasets stored in data warehouses,
databases, and complex data sources such as time-series data or spatial data.
The goal is to identify patterns, correlations, and relationships between data
items.
Often, the results obtained from data mining are later used as input for
machine learning models.
What is Machine Learning?
Machine Learning is a field of computer science that focuses on developing
systems that can learn from data without being explicitly programmed.
The term Machine Learning was introduced by Arthur Samuel in 1959, a pioneer
in artificial intelligence and computer gaming. He defined machine learning as
the ability of computers to learn from experience without being explicitly
programmed.
Machine learning systems use algorithms that analyze data, identify patterns,
and build predictive models. These models allow the system to make decisions
or predictions when new data is provided.
The algorithms improve automatically as more training data is provided.
The main goal of machine learning is to build models from data that can make
accurate predictions or decisions.
Types of Machine Learning
Machine learning algorithms are mainly divided into two categories.
1. Supervised Learning
In supervised learning, the algorithm is trained using labeled data. This
means the correct output is already known.
The model learns from this labeled dataset and later uses this knowledge to
predict results for new data.
Example:
- Email spam detection
- House price prediction
2. Unsupervised Learning
In unsupervised learning, the data does not contain labeled outputs. The
algorithm tries to identify hidden patterns or structures in the data on its
own.
Common techniques used in unsupervised learning include:
- Clustering
- Association
Example:
- Customer segmentation
- Market basket analysis
