.png){kind=icon}
Return
A categorical data clustering framework on graph representation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2022.108694.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Clustering categorical data is an important task of machine learning, since the type of data widely exists in real world. However, the lack of an inherent order on the domains of categorical features prevents most of classical clustering algorithms from being directly applied for the type of data. Therefore, it is very key issue to learn an appropriate representation of categorical data for the clustering task. In order to address this issue, we develop a categorical data clustering framework based on graph representation. In this framework, a graph-based representation method for categorical data is proposed, which learns the representation of categorical values from their similar graph to provide similar representations for similar categorical values. We compared the proposed framework with other representation methods for categorical data clustering on benchmark data sets. The experiment results illustrate the proposed frame-work is very effective, compared to other methods. (c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Cluster analysis
Categorical data clustering
Data representation
Graph embedding
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
Potential Negative Consequences of Adding Phosphorus‐Based Fertilizers to Immobilize Lead in Soil
PREAI
ShareSave
ShareSaveShareSaveUnsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling framework.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAIShareSave
View more