Data Mining vs Web Mining – A Detailed Comparison Between The Two

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Data Mining vs Web Mining

Data has become important in today’s world and is required in almost all fields to understand the ongoing and upcoming trends. This is why people are now scrambling to get their hands on as much data as they can. However, there are two different types of mining techniques for data – data mining vs web mining. Let’s take a look at what is the difference between the two.

Data Mining:

It is the process of identifying a significant pattern from data collection which gives a better outcome.

Categories of data mining are as follows:

1. Data preparation
2. Pattern discovery
3. Build models for forecast
4. Summarizing the model value

Web Mining:

It performs the process of data mining on websites and web pages It includes extracting web documents and discovering patterns from it.

Categories of web mining are as follows:

1. Web content mining
2. Web structure mining
3. Web usage mining

Head to head Comparison between Data Mining and Web Mining

 Data Mining vs Web Mining

PointsData MiningWeb Mining
ConceptPattern identification from data available in any systemsPattern identification from web data
Use Cases /Real World scenariosBusiness intelligence applications which includes information to enhance business activities.Data analytics which refers to modification of raw data into a meaningful format
Target UsersData scientist and data engineersData scientists along with data analysts ( web analysts)
Process Data extraction with pattern discovery Same process but on web using the web documents
Tools It includes tools like machine learning algorithms Special tools for web mining are Scrapy, PageRank and Apache logs
SignificanceMany organizations are relying on data mining results for decision making processWeb mining is significant to pull the existing data mining process.
Skills It includes approaches for data cleansing, machine learning algorithms. Statistics and probabilityIt includes application level knowledge, data engineering with mathematical modules like statistics and probability.

Drawback:
The most criticized and ethical issue for both data mining and web mining is the invasion of privacy feature. Privacy is considered to be lost for data mining and web mining if the individual has access to the information. The obtained data will be analyzed and clustered to form profiles. To prevent this, data will be made anonymous before clustering.

Venn Diagram:

Venn Diagram

The Venn diagram clearly depicts that web mining is a subset of data mining and includes major features of data mining. There are various algorithms which are used by both data and web mining procedures:

1. Cluster Analysis
2. Decision trees
3. Factor analysis
4. Neural Networks
5. Knowledge discovery
6. Business Intelligence
7. Structured data analysis
8. Associative rule learning

Based on the algorithms there are various types of application created for data and web mining procedure which are mentioned below:

User Profile generation:
It will include features such as web customization and will provide users with web pages and advertisements of interest.

Advertising domain:
Advertisements are the major source of income for web portals and with web mining, you can specifically achieve the same.

Information Retrieval:
Web mining and data mining tools analyze the logs of useful customer related information which will help to personalize the websites based on the behavior.

Fraud Management:
Both web mining and data mining can be used to raise alarm and understand the root cause of the fraudulent activities.

Conclusion:
The world wide web is considered as a major source of data with respect to all domains. The web users, academicians, developers and research analysts gather all the necessary information through the world wide web. Data and web mining are considered as challenging activities with the main motive to discover new, relevant information and knowledge by focusing on its content and usage. Mining techniques with the associated data are used to discover knowledge and how well it could give a better outcome. Organizations which are interested in enhancing their businesses with mining process make a high profit. They need to make many decisions based on the data that is widely available in systems. Data scientists raise questions which are solved by data analysts who work on the web mining process. In layman’s terms, data mining and web mining can be compared to the process of churning butter from milk.

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