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A Novel K-harmonic Means Clustering Based on Enhanced Firefly Algorithm

机译:基于增强萤火虫算法的新型k次谐波意味着聚类

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The K-harmonic means is a center based clustering algorithm, which is suffering from falling into local optima easily. To solve this problem, a hybrid K-harmonic means algorithm using enhanced firefly algorithm is proposed. Combining with parallel chaotic optimization, a novel chaotic local search method which has the capability of full dimensional and part dimensional searching is used to enhance the original firefly algorithm. Then this enhanced version of firefly algorithm is integrated into K-harmonic means algorithm to take full advantage of merits of both algorithms. The proposed method is compared with KHM and other two hybrid algorithms on four data sets, and the experiment results show the superiority of it that can escape from local optima effectively.
机译:k谐波装置是一种基于中心的聚类算法,它遭受易于落入本地Optima。为了解决这个问题,提出了一种使用增强型萤火虫算法的混合k谐波装置算法。结合并行混沌优化,一种新的混沌本地搜索方法,其具有完整尺寸和零件尺寸搜索能力的能力来增强原始萤火虫算法。然后,这种萤火虫算法的增强版本被集成到K谐波装置算法中,以充分利用两种算法的优点。将所提出的方法与khm和其他两个混合算法进行比较,在四个数据集上,实验结果显示了它可以有效地从本地Optima逃脱的优势。

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