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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >An Efficient Web Usage Mining Approach Using Chaos Optimization and Particle Swarm Optimization Algorithm Based on Optimal Feedback Model
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An Efficient Web Usage Mining Approach Using Chaos Optimization and Particle Swarm Optimization Algorithm Based on Optimal Feedback Model

机译：基于最优反馈模型的混沌优化和粒子群算法的高效Web使用挖掘方法

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摘要

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.
机译：信息资源的动态性质以及用户对信息需求的不断变化使得很难为数据挖掘和文档排名提供有效的方法。提出了一种基于用户反馈模型的混沌优化和粒子群优化的有效粒子群混沌优化挖掘算法，为用户提供了最匹配的网页列表。提出的算法始于在D维搜索空间中移动的许多粒子的初始种群，其中每个粒子向量对应于潜在问题的潜在解决方案，该潜在问题由网页子集形成。实验结果表明，我们的方法在响应时间，执行时间，精度和查全率方面明显优于其他算法。

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