A Survey on Mixed-Attribute Outlier Detection Methods

Authors

  • Nur Rokhman Universitas Gadjah Mada

DOI:

https://doi.org/10.21512/commit.v13i1.5558

Keywords:

Outlier Detection, Categorical Data, Numerical Data, Mixed-Attribute Data

Abstract

In the data era, outlier detection methods play 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 numerical, categorical, or mixed-attribute data. However, the study of the outlier detection methods is generally conducted for numerical data. Meanwhile, many real-life facts are presented in mixed-attribute data. In this paper, the researcher presents 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.

Dimensions

Plum Analytics

Author Biography

Nur Rokhman, Universitas Gadjah Mada

Department of Computer Sciences and Electronics

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Published

2019-05-31
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PDF downloaded 363  .