Learning-Based Dissimilarity for Clustering Categorical Data
Comparing data objects is at the heart of machine learning. For continuous data, object dissimilarity is usually taken to be object distance; however, for categorical data, there is no universal agreement, for categories can be ordered in several different ways. Most existing category dissimilarity...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-04-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/8/3509 |
id |
doaj-403359bbf997416aaf43a79a0c76eda3 |
---|---|
record_format |
Article |
spelling |
doaj-403359bbf997416aaf43a79a0c76eda32021-04-14T23:02:29ZengMDPI AGApplied Sciences2076-34172021-04-01113509350910.3390/app11083509Learning-Based Dissimilarity for Clustering Categorical DataEdgar Jacob Rivera Rios0Miguel Angel Medina-Pérez1Manuel S. Lazo-Cortés2Raúl Monroy3School of Engineering and Science, Tecnologico de Monterrey, Estado de Mexico 52926, MexicoSchool of Engineering and Science, Tecnologico de Monterrey, Estado de Mexico 52926, MexicoTecNM/Instituto Tecnológico de Tlalnepantla, Tlalnepantla de Baz 54070, MexicoSchool of Engineering and Science, Tecnologico de Monterrey, Estado de Mexico 52926, MexicoComparing data objects is at the heart of machine learning. For continuous data, object dissimilarity is usually taken to be object distance; however, for categorical data, there is no universal agreement, for categories can be ordered in several different ways. Most existing category dissimilarity measures characterize the distance among the values an attribute may take using precisely the number of different values the attribute takes (the attribute space) and the frequency at which they occur. These kinds of measures overlook attribute interdependence, which may provide valuable information when capturing per-attribute object dissimilarity. In this paper, we introduce a novel object dissimilarity measure that we call <i>Learning-Based Dissimilarity</i>, for comparing categorical data. Our measure characterizes the distance between two categorical values of a given attribute in terms of how likely it is that such values are confused or not when all the dataset objects with the remaining attributes are used to predict them. To that end, we provide an algorithm that, given a target attribute, first learns a classification model in order to compute a confusion matrix for the attribute. Then, our method transforms the confusion matrix into a per-attribute dissimilarity measure. We have successfully tested our measure against 55 datasets gathered from the University of California, Irvine (UCI) Machine Learning Repository. Our results show that it surpasses, in terms of various performance indicators for data clustering, the most prominent distance relations put forward in the literature.https://www.mdpi.com/2076-3417/11/8/3509dissimilaritycategorical dataclustering |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Edgar Jacob Rivera Rios Miguel Angel Medina-Pérez Manuel S. Lazo-Cortés Raúl Monroy |
spellingShingle |
Edgar Jacob Rivera Rios Miguel Angel Medina-Pérez Manuel S. Lazo-Cortés Raúl Monroy Learning-Based Dissimilarity for Clustering Categorical Data Applied Sciences dissimilarity categorical data clustering |
author_facet |
Edgar Jacob Rivera Rios Miguel Angel Medina-Pérez Manuel S. Lazo-Cortés Raúl Monroy |
author_sort |
Edgar Jacob Rivera Rios |
title |
Learning-Based Dissimilarity for Clustering Categorical Data |
title_short |
Learning-Based Dissimilarity for Clustering Categorical Data |
title_full |
Learning-Based Dissimilarity for Clustering Categorical Data |
title_fullStr |
Learning-Based Dissimilarity for Clustering Categorical Data |
title_full_unstemmed |
Learning-Based Dissimilarity for Clustering Categorical Data |
title_sort |
learning-based dissimilarity for clustering categorical data |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-04-01 |
description |
Comparing data objects is at the heart of machine learning. For continuous data, object dissimilarity is usually taken to be object distance; however, for categorical data, there is no universal agreement, for categories can be ordered in several different ways. Most existing category dissimilarity measures characterize the distance among the values an attribute may take using precisely the number of different values the attribute takes (the attribute space) and the frequency at which they occur. These kinds of measures overlook attribute interdependence, which may provide valuable information when capturing per-attribute object dissimilarity. In this paper, we introduce a novel object dissimilarity measure that we call <i>Learning-Based Dissimilarity</i>, for comparing categorical data. Our measure characterizes the distance between two categorical values of a given attribute in terms of how likely it is that such values are confused or not when all the dataset objects with the remaining attributes are used to predict them. To that end, we provide an algorithm that, given a target attribute, first learns a classification model in order to compute a confusion matrix for the attribute. Then, our method transforms the confusion matrix into a per-attribute dissimilarity measure. We have successfully tested our measure against 55 datasets gathered from the University of California, Irvine (UCI) Machine Learning Repository. Our results show that it surpasses, in terms of various performance indicators for data clustering, the most prominent distance relations put forward in the literature. |
topic |
dissimilarity categorical data clustering |
url |
https://www.mdpi.com/2076-3417/11/8/3509 |
work_keys_str_mv |
AT edgarjacobriverarios learningbaseddissimilarityforclusteringcategoricaldata AT miguelangelmedinaperez learningbaseddissimilarityforclusteringcategoricaldata AT manuelslazocortes learningbaseddissimilarityforclusteringcategoricaldata AT raulmonroy learningbaseddissimilarityforclusteringcategoricaldata |
_version_ |
1721526896904110080 |