Data Mining vs Text Mining vs Web Mining: 3 Critical Differences


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Data Mining is a popular subject among Customer-focused companies. Many companies rely on Data to target customers based on their personal preferences to maximize profits. Data Mining is a broader term that means mining the Data and extracting the information, which can help while making decisions, marketing strategies, building new customer relationships, and much more. 

This blog post will discuss what is Data Mining, Text Mining and Web Mining. Also, you will read about the differences between Data Mining vs Text Mining vs Web Mining. After reading this blog, you can easily differentiate between Data Mining vs Text Mining vs Web Mining and why they are used in different fields.

Table of Contents

What is Data Mining?

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Data Mining is a process of finding patterns and extracting useful information from the pool of large data sets by transforming the data with a bunch of business rules. With the help of Data Mining procedures, Raw datasets are converted into valuable datasets, which developers can further use to analyze and determine the patterns. 

Data Mining is an effective procedure for any organization as it helps improve the marketing strategies and helps them target the customer base based on the data. With the help of structured data, it also allows you to study different aspects of data and then get more innovative ideas to increase productivity and sales.

The Data Mining process breaks down into the following steps – 

  1. Collect, Extract, Transform and Load the data into the data warehouse
  2. Store and manage the data in the database or on the cloud.
  3. Provide access to data to the business analyst, management teams, and Information Technology professionals.

What is Text Mining?

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Text Mining is a subset of Data Mining, and it involves the processing of data from various text documents. It is the process of transforming unstructured text into a structured format and interpreting these data to identify patterns. In Text Mining, various deep learning algorithms are used to evaluate the text and generate useful information effectively.

The basic idea behind Text Mining is to find patterns in large datasets that can be used for various purposes. Text Mining requires both Sohistcated linguistic and statistical techniques to analyze the unstructured text format data and provide valuable insights. Text mining consists of a wide variety of methods and technologies such as:

  • Keyword-based Technologies: Keyword-based technologies depend on selecting keywords that input data contains and are then filtered as a series of character strings. 
  • Statistics Technologies: Statistical technology refers to the system that is completely based on Machine learning. It uses certain text to model the data and, in turn, uses the same model to manage and categorize text.
  • Linguistic-based Technologies: Lingustinc based system uses a Natual language processing system. The NLP models read the input text and understand the structure of the text, grammar, logic, and context of the text.
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What is Web Mining?

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Web Mining is a process of extracting various useful information readily available on the Internet (or World Wide Web). Web Mining is a subset of Data Mining. It helps to analyze user activities on different web pages and track them over a period of time to understand customers’ behavior and surfing patterns. Web Mining is broadly categorized into three main subcategories –

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There are three main types of Web Data, as shown in the above image. Let’s discuss in brief these Web Data types.

  1. Web Content Data: The widespread form of data in Web Content are HTML, web pages, images, etc. All these various data types constitute Web Content data. The main layout for the Internet/Web content is HTML, with a slight difference depending upon the use of the browser, but the basic layout structure is the same everywhere. 
  2. Web Structure Data: On a typical web page, the contents are arranged within HTML tags. The pages are hyperlinked, allowing users to navigate back and forth to find relevant information. So basically, relationship/links describing the connection between webpages is web structure data.
  3. Web Usage Data: The main Data is generated by the Web Server and Application Server on a typical web page. Web/Application server collects the log data, including information about the users like their geographical location, time, the content they interacted with, etc. The data in these log files are categorized into three types based on the source it comes from:
  • Server-side
  • Client-side
  • Proxy side.

Difference Between Data Mining vs Text Mining vs Web Mining

Now that you have a brief understanding of Data Mining, Text Mining, and Web Mining. In this section, you will read more about the differences between Data Mining vs Text Mining vs Web Mining. It will help you better understand these different Mining types. The following key differences between Data Mining vs Text Mining vs Web Mining are listed below:

Data Mining vs Text Mining vs Web Mining: Generic

The data mining process extracts, transform, and load the data into the data warehouse. The business users use these tools to present these analyzed data in a representable form such as tables, graphs, or charts. Data points such as Currencies, dates, and names are easy to link and do not require understanding their context.

On the other hand, Text mining processes the texts, which are in the form of text documents, emails, social media posts, etc. Text mining also faces significant challenges for linguistic texts and SMS languages. 

Web Data Mining is a technique that extracts data from the Web. It can be using data from Web servers or web page scrapping. Web Mining has to deal with many log files to extract relevant information.

Data Mining vs Text Mining vs Web Mining: Process

Data mining mainly focuses on data-dependent activities such as accounting, purchasing, CRM, etc. The Data is easily accessible and homogeneous. Once the algorithm is determined, it is easier to process the data and extract the relevant information. 

On the other hand, Text mining is a complex process requiring a long time to deploy. Text mining includes several steps like language guessing, tokenization, text segmentation, etc. 

The entire Data is based on the logs collected from the Web Servers on Web Data mining. Analyzing these logs are complex process as logs generally contain too much information, and hence it requires several business rules to be pre-determined before extracting data from the weblogs.

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Data Mining vs Text Mining vs Web Mining: Use Case

Data mining is a robust industrial technology used for mining data for decades.

On the other hand, Text Mining was one of the complex, domain-specific, and language-specific tools, and hence it was never valued as a ‘must-have.’ 

Web Mining is a relatively new process, and it came into existence after the origin of the World Wide Web. Web Mining is considered to be a critical mining aspect in terms of understanding user behavior over the internet. 

With the advent of digitalization, the rise of social networks, and increased connectivity, companies are now more concerned about their online reputation. They are looking for ways to increase customer loyalty in a world of increasing choices. 

Data Mining vs Text Mining vs Web Mining: Comparison Table

Base for ComparisonData Mining Text MiningWeb Mining
ConceptData mining is the statistical technique of processing the raw data into the structural form.Text mining is the subset of Data Mining that involves processing unstructured text documents into a structured format.Web mining is a subset of Data Mining that involves processing the data related to the Web. It can be Web Logs, Web Structure data, or Web Contact data.
Data RetrievalData is mined and then stored in the data warehouse. The data stored in Databases and spreadsheets are used to gather information and perform analysis.Text Data are stored in Text Documents, emails, and logs and then processed to gather high-quality information.Web Data can be in the form of Structure, Content, and usage data and is later converted into useful information.
Types of DataThe discovery of knowledge from structured Data is homogeneous and easy to access.Text Mining involves data from text documents, emails, logs, PDFs, etc.Web mining mainly deals with three types of data, i.e., Web Structure Data, Web Content Data, and Web Usage Data.
ApplicationData Mining is used in fields like medicine, marketing, healthcare, etc.Text Mining is used in the fields like customer profile analysis, bioscience, etc.Web Mining is used to extract information from the web, analyze weblogs, etc.
Data FormatIn Data Mining the data is stored in a structured formatIn Text Mining, the data is stored in an unstructured formatIn Web Mining, the data is structured as well as unstructured. The data format depends upon the type of Mining method.
Skills RequiredTo retrieve the meaningful data from Data Mining, one must be aware of Data cleansing techniques, machine learning algorithms, statistics, probabilityText mining requires pattern recognition techniques and Natural language processing to enrich the meaning of the text.In web mining, Application-level knowledge, Data engineering, statistics, and probability are required to successfully retrieve the information from weblogs.
Techniques UsedStatistical techniques are most helpful in analyzing data.In Text Mining, Computational linguistic principles are used to evaluate the meaning of the text.In web mining, Sequential pattern, clustering, and associative mining principles are used.


This blog post discussed the three different types of Mining, viz. Data Mining, Web Mining, and Text Mining. We have also uncovered the differences between Data Mining vs Text Mining vs Web Mining and understood how they are related. Data Mining vs Text Mining vs Web Mining is widely used for various business purposes. They are different from each other but serve the same wide purpose.

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Vishal Agrawal
Technical Content Writer, Hevo Data

Vishal Agarwal is a Data Engineer with 10+ years of experience in the data field. He has designed scalable and efficient data solutions, and his expertise lies in AWS, Azure, Spark, GCP, SQL, Python, and other related technologies. By combining his passion for writing and the knowledge he has acquired over the years, he wishes to help data practitioners solve the day-to-day challenges they face in data engineering. In his article, Vishal applies his analytical thinking and problem-solving approaches to untangle the intricacies of data integration and analysis.

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