An outlier detection algorithm for categorical matrix-object data

Outlier detection is a significant problem in data mining and machine learning which aims to discover objects in a data set that do not conform to well-defined notions of expected behavior. Generally, the input of the existing outlier detection algorithms is a collection of n objects and each object is described by a feature vector. However, in many real world applications, an object is not only described by one feature vector, but a number of feature vectors. In this paper, we define an object described by more than one feature vector as a matrix-object. Inspired by the concepts of cohesion and coupling in software engineering, we define the coupling of a matrix-object based on the average distance between it and other matrix-objects, and define its cohesion based on information entropy and mutual information. On this basis, the outlier factor of a matrix-object is given, and an outlier detection algorithm for categorical matrix-object data is proposed. The experimental results on real and synthetic data sets have shown that the proposed outlier detection algorithm can effectively detect outliers for the matrix-object data set compared with other algorithms. (c) 2021 Elsevier B.V. All rights reserved.