A testing-based decision tree, SigDT, is presented for clustering categorical data. The split point evaluation issue is formulated as a multiple testing problem. SigDT conducts clusterability prediction and cluster analysis simultaneously. SigDT determines the number of clusters automatically via significance testing.
Oct 26, 2024 · In this paper, we tackle the problem of interpretable categorical data clustering by growing a decision tree in a statistically meaningful manner.
In this paper, we tackle the problem of interpretable categorical data clustering by growing a decision tree in a statistically meaningful manner. We formulate ...
In this paper, we tackle the problem of interpretable categorical data clustering by growing a decision tree in a statistically meaningful manner. We formulate ...
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Significance-based decision tree for interpretable categorical data clustering ; Journal: Information Sciences, 2025, p. 121588 ; Publisher: Elsevier BV ; Authors: ...
Oct 22, 2024 · Clustering is a complex unsupervised method used to group most similar observations of a given dataset within the same cluster.
Sep 1, 2024 · The decision tree model is widely recognized as an inter- pretable model in machine learning and is commonly used for classification and ...
Time to shine for the decision tree! Tree based models split the data multiple times according to certain cutoff values in the features. Through splitting, ...
Feb 15, 2024 · We propose an iterative method to extract high-density clusters with the help of decision-tree-based classifiers as the most intuitive learning method.
Nov 17, 2024 · This article provides a detailed overview of decision trees, covering their structure, how they work, and key concepts like Gini Impurity, Entropy, and ...