The k-modes type clustering plus between-cluster information for categorical data
The k-modes type clustering plus between-cluster information for categorical data
复制标题
k-modes 类型聚类加上分类数据的簇间信息
DOI：
10.1016/j.neucom.2013.11.024
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发表时间：
2014-06-10
期刊：
影响因子：
6
通讯作者：
Liang, Jiye
中科院分区：
文献类型：
--
作者：
Bai, Liang;Liang, Jiye
k-modes算法及其改进版本被广泛用于分类数据的聚类。然而，在这些算法的迭代过程中，更新公式，如分区矩阵，聚类中心和属性权重，计算的基础上，类内信息。由于没有考虑类间信息，可能导致聚类结果中类间分离度较弱。因此，在本文中，我们提出了一个新的术语，用于反映分离。此外，新的优化目标函数的开发，通过添加建议的条款，现有的几个k-modes算法的目标函数。在优化框架下，严格推导了相应的修正公式和迭代过程的收敛性。上述改进用于增强这些现有k模式算法的有效性，同时保持它们简单。在UCI（University of加州Irvine）机器学习库的真实的数据集上进行的实验表明，改进后的算法在分类数据集上的聚类性能优于原算法，并且由于其线性时间复杂度与数据对象、属性或簇的数量有关，也可扩展到大数据集. (C)2014爱思唯尔有限公司版权所有。
The k-modes algorithm and its modified versions are widely used to cluster categorical data. However, in the iterative process of these algorithms, the updating formulae, such as the partition matrix, cluster centers and attribute weights, are computed based on within-cluster information only. The between-cluster information is not considered, which maybe result in the clustering results with weak separation among different clusters. Therefore, in this paper, we propose a new term which is used to reflect the separation. Furthermore, the new optimization objective functions are developed by adding the proposed term to the objective functions of several existing k-modes algorithms. Under the optimization framework, the corresponding updating formulae and convergence of the iterative process is strictly derived. The above improvements are used to enhance the effectiveness of these existing k-modes algorithms whilst keeping them simple. The experimental studies on real data sets from the UCI (University of California Irvine) Machine Learning Repository illustrate that these improved algorithms outperform their original counterparts in clustering categorical data sets and are also scalable to large data sets for their linear time complexity with respect to either the number of data objects, attributes or clusters. (C) 2014 Elsevier B.V. All rights reserved.