Abstract: The traditional fuzzy clustering algorithm is sensitive to initial point and easy to fall into local optimum. In order to overcome these flaws, a novel Web search results clustering method based on Fuzzy C-Mean algorithm which combines the modified Shuffled Frog Leaping Algorithm（SFLA） is presented. The new method uses SFLA to replace the iteration process of FCM based on the gradient descent. In this SFLA, a chaotic local search is introduced to improve the quality of the initial individual. In addition, mutation operating is joined to generate new individual. Simultaneously, a new searching strategy is presented to increase the optimization?ability. The experimental results show the proposed method improves the search capability and the clustering performance of fuzzy clustering algorithm, and it has the advantages in the global search ability.

Key words: Web search results clustering, Shuffled Frog Leaping Algorithm（SFLA）, Fuzzy C-Mean（FCM）, searching strategy

摘要： 针对模糊聚类算法中存在的对初始值敏感、易陷入局部最优等问题，提出了一种融合改进的混合蛙跳算法（SFLA）的模糊C均值算法（FCM）用于Web搜索结果的聚类。新算法中，使用SFLA的优化过程代替FCM的基于梯度下降的迭代过程。改进的SFLA通过混沌搜索优化初始解，变异操作生成新个体，并设计了一种新的搜索策略，有效地提高了算法寻优能力。实验结果表明，该算法提高了模糊聚类算法的搜索能力和聚类精度，在全局寻优能力方面具有优势。

关键词: Web搜索结果聚类, 混合蛙跳算法, 模糊C均值, 搜索策略