Exploiting Maximal Emerging Patterns for Classification
| 详细信息 | 推荐本文 | |
- 作者：Zhou Wang ; Hongjian Fan ; Kotagiri Ramamohanarao
- 关键词：Emerging Patterns ; classification ; Bayesian learning ; maximal Emerging Patterns
- 刊名：Lecture Notes in Computer Science
- 出版时间：2004
- 出版年：2004
- 期刊代码：99_0302-9743
- 类别：cp
- 卷：3339
- 期：1
- 数据来源：sp
摘要
Classification is an important data mining problem. Emerging Patterns (EPs) are itemsets whose supports change significantly from one data class to another. Previous studies have shown that classifiers based on EPs are competitive to other state-of-the-art classification systems. In this paper, we propose a new type of Emerging Patterns, called Maximal Emerging Patterns (MaxEPs), which are the longest EPs satisfying certain constraints. MaxEPs can be used to condense the vast amount of information, resulting in a significantly smaller set of high quality patterns for classification. We also develop a new overlapping or intersection based mechanism to exploit the properties of MaxEPs. Our new classifier, Classification by Maximal Emerging Patterns (CMaxEP), combines the advantages of the Bayesian approach and EP-based classifiers. The experimental results on 36 benchmark datasets from the UCI machine learning repository demonstrate that our method has better overall classification accuracy in comparison to JEP-classifier, CBA, C5.0 and NB. Keywords: Emerging Patterns, classification, Bayesian learning, maximal Emerging Patterns.