Web scraping and data mining looks quite the same in terms of word similarity however they are distinctively different . The distinction between the two terminologies is hard to define for many people and creates confusion when sometimes both terminologies are used interchangeably.
Today we look more in detail about two most popular and widely used terminologies data scraping and data mining and understand the difference between them, the purpose for which they are deployed and how they work etc.
About Web scraping
Data scraping is a technique in which a system program extracts a set of data with the help of output generated from another program. This technique is also referred to as Web scraping which is defined as the process of extraction of useful information from a website.
Web scraping can be used for a number of reasons such as financial and academics. These strategies may be used by an organization to gather data about its competitors and get competitive edge in the market and upscale sales numbers. It is also used for creating leads online and attracting more customers.
Web scraping collects web content and PDF data files , HTML documents, and interactive pages. These are used to market, advertise, promote brands, and social media is a platform to advertise products and services and help to generate a number of leads.
About Data mining
Data mining is a very powerful technique which helps organizations , researchers, and individuals to extract valuable information from huge data sets. Data mining is also known as Knowledge Discovery in Database (KDD Process) which includes
- data cleaning,
- data integration,
- data selection,
- data transformation,
- data mining,
- pattern evaluation and
- knowledge presentation.
Data mining represents extraction to identify patterns, trends and useful data which would allow businesses to take data driven informed decisions from huge data sets. An automatic search for large stores of information to find trends and patterns to have complex analysis procedures. It utilizes complex mathematical algorithms for data segments and evaluates the probability of future events.
Data mining can be of several types such as Relational database which is a collection of multiple data sets formally organized into tables, records and columns from which data can be accessed in various ways without having to identify the database tables.
Data Warehouse collects the data from various sources and provides meaningful business insights. Data repositories are a group of databases where an organization has kept various types of information. Object oriented databases are a combination of object-oriented databases model and relational database model; it supports classes, objects and inheritance etc.
Transactional databases is a database management system which has potential to undo a database transaction which is not performed rightly.
Advantages of Data Mining:
- Data mining enable organizations to obtain knowledge-based data
- Enable organizations to make modifications in operation and production to improve efficiencies
- Data mining is cost efficient
- Helps to make decision making easier
- Facilities automated discovery of hidden patterns as well as prediction of behaviour and trends
- Makes it easy to process huge amount of data in short time
Comparison Table: Web scraping vs Data Mining
Below table summarizes the differences between the two terminologies:
|Definition||Process of extracting data from web sources and structuring it into a format suitable for the receiver. No processing or review of data is performed||Process of extraction of useful information, patterns, trends from huge data sets|
|Used by||Organizations , individual developers||Data engineers and data scientists perform data mining|
|Identification mechanism||Identifying structures/patterns||Based on pattern identification from data available from system|
|Technique used||Text pattern matching, HTML or DOM parsing||Machine learning algorithms are used by data mining tools|
|Use cases||Collect data for research work , weather data monitoring, web data integration, social media crawling etc.||Weather forecasting, market analysis, fraud detection , prediction of customer behaviour, credit analysis , business intelligence etc.|
|Skills required||Python knowledge, HTML , DOM Parsing||Skills required to perform data mining are machine learning algorithm usage, probability, statistics knowledge|
Download the comparison table: Web Scraping vs Data Mining