Hierarchical clustering algorithm for categorical data using a probabilistic rough set model
文献类型：期刊论文
| 作者 | Li Min; Deng Shaobo; Wang Lei; Feng Shengzhong; Fan Jianping |
| 刊名 | KNOWLEDGE-BASED SYSTEMS
 |
| 出版日期 | 2014 |
| 英文摘要 | Several clustering analysis techniques for categorical data exist to divide similar objects into groups. Some are able to handle uncertainty in the clustering process, whereas others have stability issues. In this paper, we propose a new technique called TMDP (Total Mean Distribution Precision) for selecting the partitioning attribute based on probabilistic rough set theory. On the basis of this technique, with the concept of granularity, we derive a new clustering algorithm, MTMDP (Maximum Total Mean Distribution Precision), for categorical data. The MTMDP algorithm is a robust clustering algorithm that handles uncertainty in the process of clustering categorical data. We compare the MTMDP algorithm with the MMR (Min–Min–Roughness) algorithm which is the most relevant clustering algorithm, and also compared it with other unstable clustering algorithms, such as k-modes, fuzzy k-modes and fuzzy centroids. The experimental results indicate that the MTMDP algorithm can be successfully used to analyze grouped categorical data because it produces better clustering results. |
| 收录类别 | SCI |
| 原文出处 | http://www.sciencedirect.com/science/article/pii/S0950705114001300 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/5947]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 作者单位 | KNOWLEDGE-BASED SYSTEMS |
| 推荐引用方式 GB/T 7714 | Li Min,Deng Shaobo,Wang Lei,et al. Hierarchical clustering algorithm for categorical data using a probabilistic rough set model[J]. KNOWLEDGE-BASED SYSTEMS,2014. |
| APA | Li Min,Deng Shaobo,Wang Lei,Feng Shengzhong,&Fan Jianping.(2014).Hierarchical clustering algorithm for categorical data using a probabilistic rough set model.KNOWLEDGE-BASED SYSTEMS. |
| MLA | Li Min,et al."Hierarchical clustering algorithm for categorical data using a probabilistic rough set model".KNOWLEDGE-BASED SYSTEMS (2014). |
入库方式： OAI收割
来源：深圳先进技术研究院
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