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A categorical data clustering framework on graph representation
Pattern Recognition, 2022Clustering 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 ...
Liang Bai, Jiye Liang
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Data Visualization and Analysis in Second Language Research, 2000
G. D. Garcia
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G. D. Garcia
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Fuzzy Rough Attribute Reduction for Categorical Data
IEEE transactions on fuzzy systems, 2020Classical rough set theory is considered a useful tool for dealing with the uncertainty of categorical data. The major deficiency of this model is that the classical rough set model is sensitive to noise in classification learning due to the stringent ...
Changzhong Wang +4 more
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Estimating the Optimal Number of Clusters in Categorical Data Clustering by Silhouette Coefficient
Communications in Computer and Information Science, 2019The problem of estimating the number of clusters (say k) is one of the major challenges for the partitional clustering. This paper proposes an algorithm named k-SCC to estimate the optimal k in categorical data clustering.
Duy-Tai Dinh, T. Fujinami, V. Huynh
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Clustering Categorical Data: A Survey
International Journal of Information Technology and Decision Making, 2020Clustering is a complex unsupervised method used to group most similar observations of a given dataset within the same cluster. To guarantee high efficiency, the clustering process should ensure high accuracy and low complexity.
S. Naouali +2 more
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Anomaly Detection Methods for Categorical Data
ACM Computing Surveys, 2019Anomaly detection has numerous applications in diverse fields. For example, it has been widely used for discovering network intrusions and malicious events.
Ayman E. Taha, A. Hadi
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Annual Review of Psychology, 1998
▪ Abstract  This chapter reviews recent developments in the analysis of categorical and contingency-table data. The first portion examines developments in model testing and selection. The second portion examines work on models for the structure of dependence.
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▪ Abstract  This chapter reviews recent developments in the analysis of categorical and contingency-table data. The first portion examines developments in model testing and selection. The second portion examines work on models for the structure of dependence.
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Effective Methods of Categorical Data Encoding for Artificial Intelligence Algorithms
MathematicsIt is known that artificial intelligence algorithms are based on calculations performed using various mathematical operations. In order for these calculation processes to be carried out correctly, some types of data cannot be fed directly into the ...
F. Bolikulov +4 more
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