An Efficient Web Usage Mining Approach Using Chaos Optimization and Particle Swarm Optimization Algorithm Based on Optimal Feedback Model

Abstract

The dynamic nature of information resources as well as the continuous changes in the information demands of the users has made it very difficult to provide effective methods for data mining and document ranking. This paper proposes an efficient particle swarm chaos optimization mining algorithm based on chaos optimization and particle swarm optimization by using feedback model of user to provide a listing of best-matching webpages for user. The proposed algorithm starts with an initial population of many particles moving around in aD-dimensional search space where each particle vector corresponds to a potential solution of the underlying problem, which is formed by subsets of webpages. Experimental results show that our approach significantly outperforms other algorithms in the aspects of response time, execution time, precision, and recall.

A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence

“…It gives recommendations based items liked by the users of the same interest. Dai et al [ 2 ] introduced particle swarm chaos optimization mining algorithm (PSCOMA). It uses the strong global search ability of PSO and the strong local search ability of chaos optimization for the process of web usage mining.…”

Effective Filtering of Query Results on Updated User Behavioral Profiles in Web Mining

“…The characteristics of time in rule mining process were introduced in [ 11 ] using Apriori and FP-growth algorithms with the objective of increasing the number of rules generated using temporal rule mining. In [ 12 ], browsing of user on the basis of time (i.e., time series analysis) was taken into consideration and based on the results obtained through it, optimization technique called chaos, and swarm was applied and handled the dynamics of user behavior over time.…”