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Web data mining is the application of data-mining techniques to data collected on or about the World Wide Web, with the goal of discovering useful patterns, relationships, or knowledge. The field is usually divided into three branches based on the kind of web data being analyzed: web content mining, web structure mining, and web usage mining.
What the term means
The key idea is the word “mining.” Web data mining takes methods from general data mining, such as pattern discovery, classification, clustering, and association analysis, and applies them to web-derived data. The output is not the data itself but something learned from it: a recurring pattern, a relationship between items, or a useful piece of knowledge that answers a question.
The definition covers two broad kinds of input. One is information found inside web pages. The other is information about how the web is connected and how people use it, including hyperlinks and recorded access behavior. Both fall under the same umbrella because both are discovered through data-mining techniques.
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The most widely used taxonomy sorts web data mining by the source it analyzes. The table below sets out the three branches.
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| Branch | Data examined | What it seeks |
|---|---|---|
| Web content mining | Text, images, audio, video, tables, and other content presented on pages | Useful information or patterns within the material that web documents present |
| Web structure mining | Hyperlinks and connections among web pages; some accounts also include document structure | Relationships, connectivity, and patterns in the web’s link graph |
| Web usage mining | Server logs, clickstreams, and other records of user access | Patterns in how users access web pages or applications |
Web content mining
Content mining works on what a page says and shows. A typical task is extracting product names, prices, and descriptions from many pages and grouping them, or finding topics across a set of documents. Because much web content is text, content mining overlaps heavily with text mining, though it also covers images, audio, video, and tables.
Web structure mining
Structure mining treats the web as a graph. Pages are nodes and hyperlinks are edges. Analysts study which pages are linked, how densely clusters of pages connect, and which pages sit at central positions in the link network. Some definitions extend this branch to the internal structure of a document, such as its headings, sections, and markup, but the core focus is connectivity between pages.
Web usage mining
Usage mining studies the record of how people interact with a site or application. The raw material is usually server logs or clickstreams: which pages were requested, in what order, from where, and when. Patterns found here might show common navigation paths or pages that visitors tend to request together. Usage mining is the branch most often associated with web analytics, but it is only one of the three branches.
How the three branches relate
The branches describe the main evidence a project relies on. They are not separate project goals that exclude one another. A recommendation system, for example, may combine page content with records of user behavior. The sensible way to label a project is by its principal data source and the thing its analysis targets. A project that mainly examines link structure belongs under structure mining even if it also reads a few page elements.
How a web usage mining project proceeds
Usage mining has a well-documented workflow that makes the general idea concrete. In the framework described in the web usage mining literature, the work proceeds in three phases. This sequence is specific to usage mining. It is a useful model for raw access records, but it should not be presented as a required order for every content or structure project.
1. Preprocessing
Raw logs are rarely usable as they come. Preprocessing cleans them, removes entries such as requests from automated crawlers, identifies individual sessions, and converts page requests into a form that analysis can use. Skipping or rushing this step is a common cause of misleading results.
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2. Pattern discovery
Once the data is prepared, data-mining methods look for recurring behavior. Examples include frequently co-requested pages, typical navigation sequences, and groups of visitors who behave similarly.
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Discovered patterns are then interpreted in the context of the original question. A navigation sequence is only meaningful if the analyst can say what it implies about the site, such as a confusing menu or a popular path to a product. Patterns reported without this interpretation add little.
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Web data mining compared with related terms
Three terms are often confused with web data mining. Each overlaps with it in part, and each differs in a way that matters.
- Data mining. Web data mining applies data-mining techniques, so the two share methods. The difference is the data. Traditional data mining emphasizes structured data, such as records in a database. Web data is often semi-structured or unstructured. This is a broad distinction rather than a strict boundary, because web pages can also contain structured records and tables.
- Text mining. Much web content is text, so text mining is a large part of content mining. However, web content mining also covers images, audio, video, and link and usage data are outside text mining altogether.
- Web analytics. Analytics typically reports on traffic and visitor behavior, which corresponds to usage mining. Web data mining is broader because it also includes content and structure analysis, and its focus is on discovering patterns and knowledge rather than producing standard reports.
Common misunderstandings
- Web data mining is not the same as scraping. Collecting or extracting pages can supply inputs to a mining project, but collection alone does not discover patterns. Mining begins once the collected data is analyzed for useful knowledge.
- Web data mining is not the same as web analytics. Usage analysis is one of three branches. Content and structure mining belong to the same field.
- Web data is not all unstructured. Web data includes unstructured text, semi-structured markup, and structured tables and records. The mining approach depends on which form is present.
- The definition does not settle legal or technical rules. The definition establishes what the field covers. It does not determine which methods, tools, or data-access permissions apply to a particular deployment.
A standard statement of the definition
In the abstract of “Web Mining – Concepts, Applications and Research Directions,” Jaideep Srivastava, Prasanna Desikan, and Vipin Kumar write: “Web mining, i.e. the application of data mining techniques to extract knowledge from Web content, structure, and usage, is the collection of technologies to fulfill this potential.” The sentence captures the three-part scope that most later definitions follow.
Further reading
For a textbook treatment, Bing Liu’s Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data covers web content, structure, and usage mining along with related algorithms. Its second edition is published by Springer and is a useful optional reference for readers who want to go beyond definitions into methods.
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