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首页> 外文期刊>International journal of software science and computational intelligence >Chaotic Tornadogenesis Optimization Algorithm for Data Clustering Problems
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Chaotic Tornadogenesis Optimization Algorithm for Data Clustering Problems

机译：数据聚类问题的混沌遗传生成优化算法

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>This article describes how clustering is an attractive and major task in data mining in which particular set of objects are grouped according to their similarities based on some criteria. Among the numerous algorithms, k-Means is the best and efficient in address clustering problems. Any expert system is said to be good, only if it returns the optimal data clusters. The challenge of optimal clustering lies in finding the optimal number of clusters and identifying all the data groups correctly which is a NP-hard problem. Recently a new optimization algorithm TOA was developed to address these problems. However, the standard TOA is too often trapped at the local optima and premature convergence. To overcome this, this article proposes CTOA. The main objective of embedding chaotic maps into standard TOA is to compute and automatically adapt the internal parameters. The proposed CTOA is first benchmarked on standard mathematical functions and later applied to 10 data clustering problems. The obtained graphical and statistical results along with comparisons illustrate the capabilities of CTOA regarding accuracy and robustness
机译：>本文介绍了聚类如何在数据挖掘中是一项有吸引力的主要任务，在该过程中，根据对象的相似性根据某些条件对特定对象集进行分组。在众多算法中，k-Means是解决地址聚类问题的最佳方法。任何专家系统都只有在返回最佳数据簇的情况下才被认为是好的。最佳聚类的挑战在于找到最佳数目的聚类并正确识别所有数据组，这是一个NP难题。最近，开发了一种新的优化算法TOA来解决这些问题。但是，标准TOA经常陷入局部最优和过早收敛的状态。为了克服这个问题，本文提出了CTOA。将混沌图嵌入标准TOA的主要目的是计算并自动调整内部参数。建议的CTOA首先以标准数学函数为基准，然后应用于10个数据聚类问题。所获得的图形和统计结果以及比较结果说明了CTOA在准确性和鲁棒性方面的功能

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