Patterns discovery on complex diagnosis and biological data using fuzzy latent variables

Zong Xian Yin, Jung Hsien Chiang

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

This paper proposes a new clustering algorithm referred to as the Possibilitic Latent Variables (PLV) clustering algorithm. This algorithm provides a powerful tool for the analysis of complex data, such as clinical diagnosis and biological expressions data, due to its robustness to various data distributions and its accuracy in establishing appropriate groups from data. The algorithm combines a distribution model and the fuzzy degrees concept. Compared to the expectation-maximization (EM) algorithm, which is a well-known distribution estimating algorithm, the PLV algorithm has the considerable advantage that it can be applied to various data types, i.e. it is not restricted solely to Gaussian data distributions. Additionally, the proposed algorithm has a better performance than the well-known fuzzy clustering algorithm, i.e. the FCMalgorithm, where it can address compact regions, other than simply dividing objects into several equal populations. The performance of the proposed algorithm is verified by conducting clustering tasks on the contents of several medical diagnosis and biological expressions datasets.

原文English
主出版物標題23rd International Conference on Data Engineering, ICDE 2007
頁面576-585
頁數10
DOIs
出版狀態Published - 2007 九月 24
事件23rd International Conference on Data Engineering, ICDE 2007 - Istanbul, Turkey
持續時間: 2007 四月 152007 四月 20

出版系列

名字Proceedings - International Conference on Data Engineering
ISSN(列印)1084-4627

Other

Other23rd International Conference on Data Engineering, ICDE 2007
國家Turkey
城市Istanbul
期間07-04-1507-04-20

    指紋

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

引用此

Yin, Z. X., & Chiang, J. H. (2007). Patterns discovery on complex diagnosis and biological data using fuzzy latent variables. 於 23rd International Conference on Data Engineering, ICDE 2007 (頁 576-585). [4221706] (Proceedings - International Conference on Data Engineering). https://doi.org/10.1109/ICDE.2007.367903