Data Harvesting vs Data Mining
Data harvesting and data mining are both important processes used to handle
data effectively.
They help organizations collect, organize, and analyze data so they can make
better decisions
and improve services.
What is Data Harvesting?
Data harvesting is the process of collecting data from online sources such
as websites. It is also
known as web scraping, web crawling, or data extraction.
The term “harvesting” comes from agriculture, where crops are collected from
fields. Similarly, in
data harvesting, useful information is gathered from the internet and stored
in a structured
format like a database.
How it works:
- Automated tools (called crawlers or scrapers) scan websites
- They extract useful data
- The data is then saved in a structured format (like Excel or databases)
Key points:
- Focuses only on collecting data
- Does not use machine learning or complex analysis
- Uses programming languages like Python, Java, or R
- Tools like Octoparse can automate the process
What is Data Mining?
Data mining is the process of analyzing large amounts of data to find
patterns, trends, and
useful insights.
Unlike data harvesting, data mining is not about collecting data—it is about
understanding and
learning from the data.
It combines:
- Statistics
- Machine Learning
- Computer Science
Data mining is also known as Knowledge Discovery from Data (KDD)
Key Applications of Data Mining
1. Classification
Classification means grouping data into categories.
Example:
Banks analyze customer details (income, job, etc.) to decide whether a loan
applicant is low-risk
or high-risk.
2. Regression
Regression is used to predict future values based on past data.
Example:
Predicting crime rates in a specific area using historical data.
3. Clustering
Clustering means grouping similar data points together.
Example:
E-commerce platforms like Amazon group similar products to help users find
items easily.
4. Anomaly Detection
This is used to identify unusual or abnormal patterns.
Example:
Banks detect fraud by spotting unusual transactions.
5. Association Learning
Association learning finds relationships between items.
Example:
Customers who buy soft drinks may also buy snacks. This is used in market
basket analysis.
Difference Between Data Harvesting and Data Mining
Both data harvesting and data mining deal with data, but they serve
different purposes.
Data Harvesting
- Data harvesting means collecting data from websites or sources.
- It focuses on gathering useful information that businesses can use.
- The main goal is to understand customer needs and behavior.
- It gives immediate insights based on what users are saying or doing.
- It can be done manually or automatically.
- The process is simple and can be done even by beginners.
- It mainly involves extracting and storing data for future use.
- Another name for data harvesting is data scraping.
- Example tools: Import.io, Octaparse, Web Scraper, Visual Web Ripper.
Data Mining
- Data mining means analyzing large amounts of data to find patterns and insights.
- It focuses on understanding trends and predicting future behavior.
- The main goal is to make better business decisions using data.
- It provides long-term and predictive solutions.
- It is mostly an automated process using algorithms.
- It requires skilled professionals and expertise.
- It converts raw data into useful reports and knowledge.
- Another name for data mining is Knowledge Discovery in Databases (KDD).
- Example tools: RapidMiner, Weka, KNIME, Orange, Sisense
