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Automatic Clustering Using Teaching Learning Based Optimization

机译：使用基于教学学习的优化进行自动聚类

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Finding the optimal number of clusters has remained to be a challenging problem in data mining research community. Several approaches have been suggested which include evolutionary computation techniques like genetic algorithm, particle swarm optimization, differential evolution etc. for addressing this issue. Many variants of the hybridization of these approaches also have been tried by researchers. However, the number of optimal clusters and the computational efficiency has still remained open for further research. In this paper, a new optimization technique known as “Teaching-Learning-Based Optimization” (TLBO) is implemented for automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified rather it determines the optimal number of partitions of the data “on the run”. The new AUTO-TLBO algorithms are evaluated on benchmark datasets (collected from UCI machine repository) and performance comparisons are made with some well-known clustering algorithms. Results show that AUTO-TLBO clustering techniques have much potential in terms of comparative results and time of computations.
机译：在数据挖掘研究社区中，找到最佳数量的集群仍然是一个具有挑战性的问题。为了解决这个问题，已经提出了几种方法，包括进化计算技术，例如遗传算法，粒子群优化，差分进化等。研究人员还尝试了这些方法杂交的许多变体。但是，最佳聚类的数量和计算效率仍然有待进一步研究。在本文中，一种称为“基于教学学习的优化”（TLBO）的新优化技术被实现用于大型未标记数据集的自动聚类。与大多数现有的聚类技术相比，所提出的算法不需要先验知识就可以对数据进行分类，而是可以确定“运行中”数据的最佳分区数。新的AUTO-TLBO算法在基准数据集（从UCI计算机存储库收集）中进行了评估，并使用一些著名的聚类算法进行了性能比较。结果表明，AUTO-TLBO聚类技术在比较结果和计算时间方面具有很大的潜力。

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