Mining of association rules from frequent patterns has recently been a large field of interest in data mining studies. In addition to that, the demand of mining association rules from large web log data is increasing rapidly. When we discover hidden information from large web log data is known as web data mining. The main objective behind this mining is obtaining information regarding navigational behavior of the web users that can be used for system improvement, advertising purpose, e-commerce or business application as well as understanding user reaction, network communication etc. In this paper, we have tried to introduce a new algorithm which mines association rules from a web log dataset in which access of the users to different pages are given in some sequence of page visits. The analysis is performed over the server log dataset to generate the required association rules. Experiments have been done with our algorithm using large web log data and considerable improvement has been found.
Jabed Al Faysal, Md. Anisur Rahman, Rokebul Anam (2020); An Efficient Approach for Mining Association Rules from Web Log Data; International Journal of Scientific and Research Publications (IJSRP)
10(12) (ISSN: 2250-3153), DOI: http://dx.doi.org/10.29322/IJSRP.10.12.2020.p10876