Graph-based unsupervised feature selection for interval-valued information system
W Xu, M Huang, Z Jiang, Y Qian - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
Feature selection has become one of the hot research topics in the era of big data. At the
same time, as an extension of single-valued data, interval-valued data with its inherent …
same time, as an extension of single-valued data, interval-valued data with its inherent …
[HTML][HTML] Fuzzy clustering of spatial interval-valued data
In this paper, two fuzzy clustering methods for spatial interval-valued data are proposed, ie
the fuzzy C-Medoids clustering of spatial interval-valued data with and without entropy …
the fuzzy C-Medoids clustering of spatial interval-valued data with and without entropy …
Fuzzy clustering with entropy regularization for interval-valued data with an application to scientific journal citations
In recent years, the research of statistical methods to analyze complex structures of data has
increased. In particular, a lot of attention has been focused on the interval-valued data. In a …
increased. In particular, a lot of attention has been focused on the interval-valued data. In a …
Open writer identification from offline handwritten signatures by jointing the one-class symbolic data analysis classifier and feature-dissimilarities
MA Djoudjai, Y Chibani - International Journal on Document Analysis and …, 2023 - Springer
Usually, a large number of reference signatures are required for building the writing style
model from offline handwritten signatures (OHSs). Moreover, the existing writer identification …
model from offline handwritten signatures (OHSs). Moreover, the existing writer identification …
Comparison between two algorithms for computing the weighted generalized affinity coefficient in the case of interval data
From the affinity coefficient between two discrete probability distributions proposed by
Matusita, Bacelar-Nicolau introduced the affinity coefficient in a cluster analysis context and …
Matusita, Bacelar-Nicolau introduced the affinity coefficient in a cluster analysis context and …
Self-adaptive interval dominance-based feature selection for monotonic classification of interval-valued attributes
J Chen, Z Li, H Su, J Zhai - International Journal of Machine Learning and …, 2023 - Springer
Dominance rough set theory is a key mathematical tool for addressing monotonic
classification tasks (MCTs). However, current dominance rough set models for feature …
classification tasks (MCTs). However, current dominance rough set models for feature …
A maximum-entropy fuzzy clustering approach for cancer detection when data are uncertain
(1) Background: Cancer is a leading cause of death worldwide and each year,
approximately 400,000 children develop cancer. Early detection of cancer greatly increases …
approximately 400,000 children develop cancer. Early detection of cancer greatly increases …
Divisive Clustering for Interval Data Based on Principal Components
L Billard, J Zhu - Available at SSRN 4391366 - papers.ssrn.com
Abstract Chavent's [1-2] monothetic divisive clustering algorithm has been used extensively
for clustering interval-valued observations. To overcome some limitations of this algorithm …
for clustering interval-valued observations. To overcome some limitations of this algorithm …