Towards an efficient framework for web user behavioural pattern mining

Distributed computing innovations, such as web applications delivered via the Internet, are capable of providing a wide range of functionalities to end users. Online usage is common in all domains, such as e-commerce, net banking, and mobile networking, to name a few. Web application users exhibit a variety of behavioural patterns. It is critical for organisations to identify such patterns in order to comprehend user behaviour in terms of habits, preferences, and subtle characteristics. This will assist them in making strategic decisions. The behavioural patterns of a user during his or her session with a web application provide valuable insights. In today's world, capturing such patterns is critical for organizational development. The current state of the art in this research has made significant progress. It is, however, an open problem for optimization. In this paper, we proposed the UBPMine framework. It considers data structure that increases speed while mining user behavioural patterns. It focuses on both general user behavioural patterns and group-specific user behaviour patterns. To extract user behavioural patterns, clustering and frequent item set mining algorithms are defined with batch and streaming processing. An empirical study is carried out using a prototype application. The experimental results show that the proposed UBPMine framework performs the best frameworks for Web User Behavioural Pattern Mining.