基于自适应混沌粒子群的Web搜索结果聚类研究

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.

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

A fuzzy C-means clustering algorithm based on adaptive chaotic particle swarm optimization (ACPSO) is pro-posed 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- nois-ing, 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.