中文摘要：

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。

英文摘要：

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 modi- fied 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 in- crease the optimization ability. The experimental results show the proposed method improves the search capability and the clus- tering performance of fuzzy clustering algorithm, and it has the advantages in the global search ability.