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首页> 外文期刊>Journal of Discrete Mathematical Sciences and Cryptography >Density-based o-means clustering algorithm using minimum spanning tree
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Density-based o-means clustering algorithm using minimum spanning tree

机译：基于最小生成树的基于密度的o均值聚类算法

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摘要

Clustering is a process of discovering group of objects such that the objects of the same group are similar, and objects belonging to different groups are dissimilar. A number of clustering algorithms exist that can solve the problem of clustering, but most of them are very sensitive to their input parameters. k-Means is one of the most well known algorithms yet it suffers major shortcomings like initialize number of clusters and seed values preliminary and convergence to local minima. Minimum Spanning Tree clustering algorithm is capable of detecting clusters with irregular boundaries. Many systems in Science and Engineering can be modeled as graph. Graph based clustering algorithms aimed to find hidden structures from objects. In this paper we present a new clustering algorithm DBOMCMST using Minimum Spanning Tree. The newly proposed DBOMCMST algorithm combines the features of center-based partitioned and density-based methods using Minimum Spanning Tree removes major shortcomings like initialize number of clusters and seed values preliminary and convergence to local minima of the k-Means algorithm. Outliers can significantly affect data mining performance, so outlier detection and removal is an important task in wide variety of data mining applications. Our algorithm also detects and removes outliers from the given data set.
机译：聚类是发现一组对象的过程，以使同一组的对象相似，而属于不同组的对象却不相似。存在许多可以解决聚类问题的聚类算法，但是大多数聚类算法对其输入参数非常敏感。 k-Means是最广为人知的算法之一，但它还存在一些主要缺点，例如初始化簇数和初始种子值以及收敛到局部最小值。最小生成树聚类算法能够检测具有不规则边界的聚类。科学与工程中的许多系统都可以建模为图形。基于图的聚类算法旨在从对象中找到隐藏的结构。在本文中，我们提出了一种使用最小生成树的新聚类算法DBOMCMST。新提出的DBOMCMST算法结合了基于中心的分区和基于密度的方法的特征，使用最小生成树消除了主要缺点，如初始化簇数和种子值初步以及收敛到k-Means算法的局部最小值。离群值会严重影响数据挖掘的性能，因此离群值的检测和删除是各种数据挖掘应用程序中的重要任务。我们的算法还可以检测并从给定的数据集中删除异常值。

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