Applying hybrid Kepso clustering to web pages

ACM SE '10 Pub Date : 2010-04-15 DOI:10.1145/1900008.1900027
Teng-Sheng Moh, Ameya Sabnis
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引用次数: 1

Abstract

Various optimization methods are used along with the standard clustering algorithms to make the clustering process simpler and quicker. In this paper we propose a new hybrid technique of clustering known as K-Evolutionary Particle Swarm Optimization (KEPSO) based on the concept of Particle Swarm Optimization (PSO). The proposed algorithm uses the K-means algorithm as the first step and the Evolutionary Particle Swarm Optimization (EPSO) algorithm as the second step to perform clustering. The experiments were performed using the clustering benchmark data. This method was compared with the standard K-means and EPSO algorithms. The results show that this method produced compact results and performed faster than other clustering algorithms. Later, the algorithm was used to cluster web pages. The web pages were clustered by first cleaning the unnecessary data and then labeling the obtained web pages to categorize them.
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将混合Kepso聚类应用于网页
在标准聚类算法的基础上,采用了多种优化方法,使聚类过程更简单、更快。本文基于粒子群优化(PSO)的概念,提出了一种新的混合聚类技术——k -进化粒子群优化(KEPSO)。该算法采用K-means算法作为第一步,进化粒子群优化(EPSO)算法作为第二步进行聚类。实验采用聚类基准数据进行。将该方法与标准K-means和EPSO算法进行了比较。结果表明,该方法的聚类结果紧凑,速度快于其他聚类算法。随后,该算法被用于网页聚类。该方法首先清除不必要的数据,然后对得到的网页进行标记,对其进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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