원문정보
초록
영어
Most of the existing outlier detection methods aim at numerical data, but there will be a large number of categorical data in real life. Some outlier detection algorithms have been designed for categorical data. There are two main problems of outlier detection for categorical data, which are the similarity measure between categorical data objects and the detection efficiency problem. A cloud model-based outlier detection algorithm for categorical data is proposed in this paper. The algorithm is based on data driven idea and does not require the user to specify parameters. We utilize the synthetic data set and real data set to verify, compare our algorithm with the existing outlier detection algorithms for categorical data, and the experimental result demonstrates that our proposed algorithm has a higher detection rate and lower false alarm rate, while the time complexity is also more competitive.
목차
1. Introduction
2. Related Work
3. Principle of Cloud Model
3.1. Concept and Digital Characteristics of Cloud Model
3.2. Cloud Generator
4. Categorical Data Feature Extraction
5. Cloud Model-based Outlier Detection for Categorical Data
6. Experiments and Analysis
6.1. Experimental Setup
6.2. Analysis for Results
7. Conclusions
Acknowledgments
References