Issues,Challenges and Tools of Clustering Algorithms
Issues,Challenges and Tools of Clustering Algorithms
复制标题
DOI：
--
复制
发表时间：
2011-10
期刊：
影响因子：
--
通讯作者：
Parul Agarwal;M. A. Alam;R. Biswas
中科院分区：
文献类型：
--
作者：
Parul Agarwal;M. A. Alam;R. Biswas
聚类是一种无监督的数据挖掘技术。它意味着将相似的对象分组在一起，并将不相似的对象分开。在聚类过程中，使用距离度量为数据集中的每个对象分配一个类标签。本文抓住了实现聚类算法在现实中所面临的问题。它还考虑了最广泛使用的工具，这些工具随时可用，并支持简化编程的功能。算法一旦实现，还需要对其有效性进行测试。本文还讨论了用于测试性能和准确性的几种验证指标。
Clustering is an unsupervised technique of Data Mining. It means grouping similar objects together and separating the dissimilar ones. Each object in the data set is assigned a class label in the clustering process using a distance measure. This paper has captured the problems that are faced in real when clustering algorithms are implemented .It also considers the most extensively used tools which are readily available and support functions which ease the programming. Once algorithms have been implemented, they also need to be tested for its validity. There exist several validation indexes for testing the performance and accuracy which have also been discussed here.