Data Modeling for Web Usage Mining Simplified: Complete Guide 101

Vishal Agrawal • Last Modified: December 29th, 2022

Data Modeling for Web Usage Mining

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 make decisions, marketing, build customer relationships, and many more. This blog post will discuss Web Usage Mining, a subset of Data Mining. Without delay, let’s have a quick look at the topics that we will be covering in this article!

Table of Contents

What is Web Usage Mining?

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

Subcategories of Data Mining, Web Usage Mining | Hevo Data
Image Source

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 is HTML, web pages, images, etc. All these 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. Web Structure Data is simply relationships/links that describe the connection between web pages.

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

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What are the Data Modeling Techniques used for Web Usage Mining?

When the user visits a web page, they leave a lot of information that web servers can collect in logs. There is geographical information, the path through the pages they have accessed on the webpage, and a lot more. In a typical scenario, four kinds of Data Mining techniques are applied to extract the information generated by users on the Web. Let’s discuss each of these techniques in detail:

1. Association Rule Mining

Association Rule is one of the basic Data Mining methods used frequently by developers for Web Usage Mining. This method enables the website to track users’ information and provide recommendations based on their search history and behaviour. 

The basic principle of the Association rule contains two parts – an Antecedent (if condition) and a Consequent (then condition). An Antecedent is an item found within data, and the Consequent is the item found in combination with the Antecedent. Consider a consumer search on the e-commerce website for Protein Powder (Antecedent). The Consequent for this Antecedent product could be different protein powders, mass gainers, Protein Shakers, and so on.

Association rules in Web Usage Mining are used to find relationships between pages that frequently appear next in user sessions.

2. Sequential Patterns

Sequential patterns are used to discover the sequence in a large volume of Sequential data. Users’ navigational patterns are discovered using sequential patterns in Web Usage Mining. The sequential patterns are built over time, which means that the sequence of events is defined in sequential patterns. There are two types of algorithms used to generate sequential patterns. 

The first algorithm to identify sequential patterns is based on Association Rules Mining. GSP and AprioriAll, for instance, are two established Apriori algorithms for extracting association rules.

The second algorithm to identify sequential patterns uses tree structure and Markov chains to represent the sequential patterns. For example, in one of these algorithms called WAP-mine, the tree structure called WAP-tree is used to explore access patterns to the Web. 

3. Clustering

Clustering is a technique to create a cluster or arrange similar items in a group among a high volume of data. This clustering can be done by using the distance function, which calculates the degree of similarities between different items. Clustering is a method of grouping comparable encounters in Web Usage Mining.

There are two different types of clustering methods available:

  • User Clustering
  • Page Clustering

4. Classification Mining

Classification Mining is based on developing a profile of items that belong to a particular group classified according to their common attributes. The profile can classify any new items added and update the database accordingly. 

In Web Mining, Classified Mining allows the developers to build a profile for clients who access particular items based on their demographic information available.

What are the Processes Involved in Web Mining?

1. Data Extraction

Web Mining is a complicated process and needs proper planning to extract the data from the vast volume of logs collected by the Web Server. A developer can write programs in various languages to extract the relevant information from the pool of information. 

2. Data Cleaning

The data received from the web server logs contain a lot of information, and hence it needs proper cleaning before any useful information can be extracted. Various tools on the market may execute data cleaning operations, or a developer can construct their own tool to perform cleaning activities based on the requirements.

3. Data Transformation

Developers can use different business transformations to extract essential information once the data has been cleansed and is ready for analysis. For example, the data collected from e-commerce logs can be transformed by developers to understand which product is high in demand or how users show interest in products. 

4. Building Model and Exploration

Once the Data is transformed according to the need, it is then fed to build models that can help determine certain KPIs and allow companies to explore the data or recommend products to the customer based on their behaviour.

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Which are the Top Web Mining Tools in the Market?

Web Usage Mining is a complex process, and it requires extensive planning and methods to understand and extract useful information from the pool of data. There are several tools available in the market that perform this task efficiently. Let us have a brief look at these tools:

1) R

R is a language used for statistical computing and graphics. R has been made accessible from scripting languages like Python, Perl, Ruby, etc. R has extensive modules and packages available that can help unveil and extract the information from the Web.

2) Oracle Data Mining

Oracle Data Mining is a Mining software from Oracle. The Oracle Data Mining process uses the built-in features of the Oracle Database to store the Data structurally and efficiently and maximize the scalability by making efficient use of system resources.

3) Tableau

Tableau is a company that makes interactive data visualization tools for corporate intelligence. Tableau allows instantaneous insight by transforming data into visually appealing; interactive visualizations called dashboards. This process takes only seconds or minutes rather than months or years and is achieved by using an easy-to-use, drag-and-drop interface.

4) Scrapy

Scrapy is an open-source framework that collects data from websites. It’s designed in Python and lets programmers create their own criteria for extracting data from the Internet.

What are the Advantages of Web Usage Mining?

Web Usage Mining has several advantages as it provides some useful information about the users and their activities over the Web. Some of the advantages are listed below: 

  • Government agencies are using these technologies to fight terrorism and classify threats.
  • These Data Mining tools provide information about user behaviour that help companies to build personalized relationships with their customers. 
  • Web Usage Mining helps e-commerce to do personalized marketing, resulting in higher trade volumes.
  • By using these tools, companies can increase their profits by targeting a particular customer with their interests. The company can provide offers, coupons, and discounts to the specific customer on a specific product of interest, thereby retaining the customer. 

What are the Disadvantages of Web Usage Mining?

We have seen a few advantages of Web Usage Mining above, but like a coin has two sides, Web Usage Mining has some disadvantages. Let’s discuss a few disadvantages of Web Usage Mining: 

  • Web Usage Mining can risk the privacy of the customer. When the information is put to bad use, it can be dangerous. 
  • Lack of ethical standards can lead to misuse of the customer’s information, and the customer has to suffer because of this. For example, if a customer searches for some investment options over the Web, which can then be led to spam emails, and calls trying to lure the customer into losing their hard-earned money. 


In this blog post, we discussed Web Usage Mining, how it is beneficial for companies, and how a company can maximize their profit by using the Data effectively. We have also discussed the various techniques and tools that can help us extract useful information from the Web.

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Share your experience of understanding Web Usage Mining in the comment section below! We would love to hear your thoughts.

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