- Title
- Maximising competitive advantage on e-Business websites : A data mining approach
- Creator
- Alazab, Ammar; Bevinakoppa, Savitri; Khraisat, Ansam
- Date
- 2018
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/168506
- Identifier
- vital:13821
- Identifier
-
https://doi.org/10.1109/ICBDAA.2018.8629649
- Identifier
- ISBN:9781538671283 (ISBN)
- Abstract
- Many organizations are interested in analyzing and evaluating the web data for their websites because websites are a very important platform to carry out their business. However, website evaluations face many challenges in using analytics, especially with the huge amount of data that the websites are collecting from various sources. This explosive growth in data requires a complex tool for analyzing and automatically convert the data into valuable information. However, without using a proper analysis tool, it is very difficult to understand the user's behaviour, user's interaction patterns on the website and how users involve in the site. This paper explains methods to examine, understand and visualize the huge amounts of stored data collected from the websites. In this paper, a framework is developed for identifying user's behaviours on websites. Firstly, the attributes are extracted from different websites using Google Analytics and other API tools. Secondly, data mining techniques such as clustering, classification and information gain are applied to build this framework. The findings of these study can be used to evaluate the website and provide some guidelines for the web team to increase user engagement on the website and understand the influence of user behaviour. In addition, this framework is able to identify which behaviour features influence user decisions. Our proposed framework for identifying user's behaviours on websites is tested on a large dataset that contains a variety of individual users from different websites. © 2018 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 2018 IEEE Conference on Big Data and Analytics, ICBDA 2018; Langkawi, Malaysia; 21st-22nd November 2018 p. 111-116
- Rights
- Copyright © 2018 IEEE.
- Rights
- This metadata is freely available under a CCO license
- Subject
- E-Business websites; E-commerce websites; Web analytic; Web data mining; Web structure mining
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