ACADSTAFF UGM
| Title | : | A Survey on Mixed-Attribute Outlier Detection Methods |
| Author | : |
Dr. Nur Rokhman, S.Si., M.Kom. (1) |
| Date | : | 2019 |
| Keyword | : | Outlier detection, Categorical data, Numerical data, Mixed-attribute data Outlier detection, Categorical data, Numerical data, Mixed-attribute data |
| Abstract | : | In the big data era, outlier detection plays an important role. The existence of outliers can provide clues to the discovery of new things, irregularities in a system, or illegal intruders. Based on the data, outlier detection methods can be classified into outlier detection method for numerical, categorical, or mixed-attribute data. However, the study of the outlier detection methods are generally conducted for numerical data. Meanwhile, many real-life facts are presented in mixed-attribute data. In this paper, we present a survey of outlier detection methods for mixed-attribute data. The methods are classified into four types namely categorized, enumerated, combined, and mixed outlier detection methods for mixed-attribute data. Through this classification, the methods can be easily analyzed and improved by applying appropriate functions. Finally, the paper provides directions for future work. |
| Group of Knowledge | : | Ilmu Komputer |
| Original Language | : | English |
| Level | : | Nasional |
| Status | : |
Published
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| No | Title | Action |
|---|---|---|
| 1 |
5558-24713-1-PB.pdf
Document Type : [PAK] Full Dokumen
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