Research of Web Search Results Clustering Based on Adaptive Chaos Particle Swarm Optimization

TONG Ya-la

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Research of Web Search Results Clustering Based on Adaptive Chaos Particle Swarm Optimization

更新时间：2026-09-04

- Research of Web Search Results Clustering Based on Adaptive Chaos Particle Swarm Optimization
- Vol. 27, Issue 1, Pages: 173-176(2010)
- 作者机构：
  
  湖北工业大学理学院,湖北,武汉,430068
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
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童亚拉. 基于自适应混沌粒子群的Web搜索结果聚类研究[J]. 微电子学与计算机, 2010, 27(1): 173-176.

TONG Ya-la. Research of Web Search Results Clustering Based on Adaptive Chaos Particle Swarm Optimization[J]. 2010, 27(1): 173-176.
童亚拉. 基于自适应混沌粒子群的Web搜索结果聚类研究[J]. 微电子学与计算机, 2010, 27(1): 173-176. DOI：

TONG Ya-la. Research of Web Search Results Clustering Based on Adaptive Chaos Particle Swarm Optimization[J]. 2010, 27(1): 173-176. DOI：

摘要

提出了基于自适应混沌粒子群的Web搜索结果模糊C-均值算法

用粒子群算法代替模糊C-均值算法梯度下降的迭代过程

同时引入自适应的平衡搜索策略加快算法收敛和提高去噪能力

在增强局部搜索能力的同时引导粒子群跳出局部极值点.这样不仅在一定程度上解决了网页文档不确定性的问题

而且获得快速、稳定的聚类效果.

Abstract

A fuzzy C- means clustering algorithm based on adaptive chaotic particle swarm optimization (ACPSO) is proposed in the thesis. On one hand

interactive procedure based on FCM is replaced by that of PSO; on the other hand

a balanced adaptive .search strategy is embedded so as to accelerate algorithm convergence

improve the capacity of de-noising

enforce local search ability

and escape from local optimization. It will not only solve indeterminacy of Web document in parts

but also obtain stable clustering results quickly.

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