COMP3340 Data Mining

Introduction

Web mining could be defined as the process of using the techniques of data mining and several algorithms in order to extract direct information form the internet. The primary aim of web mining is to search for patterns within the web based data (Langhnoja, Barot & Mehta, 2013).

Discussion

Real World Examples of Web Mining

The results obtained from the usage of web mining could be applied in order to understand and analyse the usage of web based data. There are several examples of web mining, which are used in everyday life. These include:

Improvement of Systems – Each customer who make use of the e-commerce system have the major expectation that the entire system would have a higher level of performance so that they would have a nice feel while using the system. Web mining could be used in order to understand the expectations of the user and thus work on them.

Site Modifications – The attractiveness of a particular website is very important in order to attract the customers. Hence the business model should focus on the basic requirements in order to make the website much more customizable and user-friendly.

Business Intelligence – The information about the use of a particular website is a critical aspect for the marketers. Web mining could be helpful for understanding the behaviours and purchase pattern of the customer based on which the business would be able to provide suggestions to their customers (Radinsky & Horvitz, 2013).

Conclusion

Hence, it could be concluded that web mining is an increasing developing domain in the current environment and has a huge potential in the future of technology. 

References

Langhnoja, S. G., Barot, M. P., & Mehta, D. B. (2013). Web usage mining using association rule mining on clustered data for pattern discovery. International Journal of Data Mining Techniques and Applications, 2(01), 144.

Radinsky, K., & Horvitz, E. (2013, February). Mining the web to predict future events. In Proceedings of the sixth ACM international conference on Web search and data mining(pp. 255-264). ACM.