Difference Between Data mining vs Web mining
Data mining: It is a concept of identifying a significant pattern from the data that gives a better outcome. Identifying patterns from where? From the data that are generated from the systems.
Web mining: The process of performing Data mining on the web is called Web mining. Extracting the web documents and discovering the patterns from it.
Example: Techniques applied for predictive analysis. (Weather forecast based on identifying the patterns from the history data)
Lets us understand the major Difference Between Data mining and Web mining in detail in this post.
Analogy
Gold is produced by the process called gold mining. It is extracted and refined from the ore. The final outcome of gold mining is the precious metal. Likewise,
to get key information (data that is worth) from a raw source, data mining technique is applied. Here the pattern discovered from the raw data source is considered precious for the data analyst/data scientists in order to proceed with the decision making that influences the business value.
Data mining
In plain terms, data mining is a concept of mining knowledge from different sets of data. The knowledge extracted is further used to provide forecasts or recommendations. The data to be mined are either available in the data warehouse or other external systems. Data could be available on different tables with its different behavioral or attributes. In order to identify the pattern, the correlation between multiple sets of data has to be identified.
Steps in Data mining
As data mining is an abstract, here is the list of steps involved,
- Data preparation
- Pattern discovery
- Build models to forecast/recommend(to mention few cases)
- Summarizing the model value
Web mining
Web mining is an abstract as there are three different types of techniques of mining.
- Web content mining
- Web structure mining
- Web usage mining
Web mining classes of information gathering
Web content mining
Data from the web pages are extracted in order to discover different patterns that give a significant insight. There are many techniques to extract the data like web scraping (for instance – scrapy and Octoparse are the well-known tools that performs the web content mining process.
One of the best example – In order to conduct an event or any program, first the organization analyze about the locations (which location is best suited to conduct the program so that there will be full attendance). In order to perform these analyses, one has to gather location-specific information about the city, state and how far the event from the invitee is’s located. Any location-specific data can be extracted from the web. That’s where the web content mining comes into the picture.
Web Structure mining
Data from hyperlinks that lead to different pages are gathered and prepared in order to discover a pattern. In order to view a person’s public profile from a blog or any other webpage, there are chances that they would embed their social media links. So, the data is not only extracted from a single source but also from the nested pages through the hyperlinks associated with each page. There are various algorithms to perform this. (Example: PageRank algorithm)
Web usage mining:
When a web application is hosted, there are plenty of web server logs that gets generated about the application’s user web activity. These logs are considered as a raw data in return meaningful data are extracted and patterns are identified.
For instance, for any e-commerce business, when they want to increase the scope of business or add an enhancement for better customer experience, user’s web activity through the application logs are monitored and data mining is applied to it.
Web mining and data mining are more or less similar techniques but web mining is all about analysis on the web. Data mining is not limited to the web. It’s a traditional process that takes place for any data analytics.
Talking about the data from the web, there are varieties of data that can be observed. It could be structured data (database data are pulled through API if it is released for public). Semi-structured data – any web activity related or even server logs pull. Or even unstructured data like images etc. (if any analysis are performed on images)
Head to Head Comparison Between Data Mining and Web Mining (Infographics)
Below is the Top 7 Comparisons Between Data mining and Web mining:
Key Differences Between Data Mining and Web Mining
The following is the difference between Data mining and Web mining are as follows:
Web mining and data mining are both nearly similar when comes to identifying the patterns. But where and what is the difference in web mining from data mining. What kind of data and data is extracted from where? These are the two ultimate aspects that bring the difference between Data mining and Web mining.
Web mining comes under data mining but this is limited to web related data and identifying the patterns. Data mining is a vast concept that involves multiple steps starting from preparing the data till validating the end results that lead to the decision-making process for an organization.
Data Mining and Web Mining Comparison Table
Below is the comparison table between Data Mining and Web Mining.
| Basis for comparison | Data Mining | Web Mining |
| Concept | Pattern identification from data available in any systems. | Pattern identification from web data. |
| Application/use cases | Weather forecast using historical weather reports | Data crawling HITS/PageRank techniques |
| Who does this? | Data scientists Data engineers |
Data scientists/Data analysts Data engineers |
| Process | Data extraction -> Pattern discovery -> Develop the feature/solve it (Algorithm) | Same process but on web using the web documents |
| Tools | Machine learning algorithms | Scrappy, PageRank, Apache logs |
| How significant | Many organizations are relying on data science results for decision making. | Web-related data pull would influence the existing data mining process. |
| Skills | Data cleansing techniques, machine learning algorithms, statistics, probability | Application level knowledge, Data engineering, statistics, probability |
Conclusion
Any mining techniques with the data are to discover the knowledge and how well it could be used to accomplish a better outcome. Organizations that are keen on enhancing their businesses and make a high profit, they need many decisions to make based on the data that are largely available in their systems generated in humongous volume. Not all data is considered to give knowledge and insights. Which, why and what are the main questions data scientists/data analysts have to think about when they prepare to identify the patterns. In a very layman’s term, data mining is like a process of churning the milk to make butter.
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