Efficient Mining of Interest Patterns on Click Stream Data
P. Dhana Lakshmi
Dr. P. Dhana Lakshmi*, Associate Professor, Dept. of CSSE, Sree Vidyanikethan Engineering College, A. Rangampet, Chittoor, (Andhra Pradesh), India.
Manuscript received on November 18, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 635-639 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B2846129219/2020©BEIESP | DOI: 10.35940/ijeat.B2846.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Nowadays, large amount of data is generated daily in e-commerce applications as click stream data. Because of the availability of this tremendous amount of data analyzing the user browsing behaviour and finding frequent navigation patterns of different web pages accessed by web users is an key element for retailers to optimize the website and personalized the web services of different e-commerce websites. User browsing behaviour is evaluated based on user interests on web pages or products. There are different parameters are considered while analyzing the click stream data for calculating frequent navigation patterns and context based customer behaviour in online data bases. In this paper we developed different models for optimizing and personalizing web service and sequential frequent patterns using the parameters: browsing path, frequently visited web pages, time duration of web pages and user interest. These novel models uses the parameters and applied on click stream data to optimize the web pages and improve the personalized recommendation.
Keywords: Data stream, FP-Growth Algorithm, CURE Clustering, Frequent patterns.