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Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
Robust clustering of high-dimensional data is an important topic because clusters in real datasets are often heavy-tailed and/or asymmetric....
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A New Matrix Feature Selection Strategy in Machine Learning Models for Certain Krylov Solver Prediction
Numerical simulation processes in scientific and engineering applications require efficient solutions of large sparse linear systems, and variants of...
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Clustering with Minimum Spanning Trees: How Good Can It Be?
Minimum spanning trees (MSTs) provide a convenient representation of datasets in numerous pattern recognition activities. Moreover, they are...
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Cluster Validation Based on Fisher’s Linear Discriminant Analysis
Cluster analysis aims to find meaningful groups, called clusters, in data. The objects within a cluster should be similar to each other and...
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A New Look at the Dirichlet Distribution: Robustness, Clustering, and Both Together
Compositional data have peculiar characteristics that pose significant challenges to traditional statistical methods and models. Within this...
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Automatic Topic Title Assignment with Word Embedding
In this paper, we propose TAWE (title assignment with word embedding), a new method to automatically assign titles to topics inferred from sets of...
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Normalised Clustering Accuracy: An Asymmetric External Cluster Validity Measure
There is no, nor will there ever be, single best clustering algorithm. Nevertheless, we would still like to be able to distinguish between methods...
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Sensitivity and Specificity versus Precision and Recall, and Related Dilemmas
Many evaluations of binary classifiers begin by adopting a pair of indicators, most often sensitivity and specificity or precision and recall....
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Clustering Longitudinal Data for Growth Curve Modelling by Gibbs Sampler and Information Criterion
Clustering longitudinal data for growth curve modelling is considered in this paper, where we aim to optimally estimate the underpinning unknown...
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Density Peak Clustering Using Grey Wolf Optimization Approach
Density peak clustering (DPC) finds the center of the cluster as the point with high density and a large distance from the center of the other...
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Finding Outliers in Gaussian Model-based Clustering
Clustering, or unsupervised classification, is a task often plagued by outliers. Yet there is a paucity of work on handling outliers in clustering....
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SNN-PDM: An Improved Probability Density Machine Algorithm Based on Shared Nearest Neighbors Clustering Technique
Probability density machine (PDM) is a novel algorithm which was proposed recently for addressing class imbalance learning (CIL) problem. PDM can...
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A Novel Classification Algorithm Based on the Synergy Between Dynamic Clustering with Adaptive Distances and K-Nearest Neighbors
This paper introduces a novel supervised classification method based on dynamic clustering (DC) and K-nearest neighbor (KNN) learning algorithms,...
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Accelerated Sequential Data Clustering
Data clustering is an important task in the field of data mining. In many real applications, clustering algorithms must consider the order of data,...
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Skew Multiple Scaled Mixtures of Normal Distributions with Flexible Tail Behavior and Their Application to Clustering
The family of multiple scaled mixtures of multivariate normal (MSMN) distributions has been shown to be a powerful tool for modeling data that allow...
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Inferential Tools for Assessing Dependence Across Response Categories in Multinomial Models with Discrete Random Effects
We propose a discrete random effects multinomial regression model to deal with estimation and inference issues in the case of categorical and...
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Prediction of Forest Fire Risk for Artillery Military Training using Weighted Support Vector Machine for Imbalanced Data
Since the 1953 truce, the Republic of Korea Army (ROKA) has regularly conducted artillery training, posing a risk of wildfires — a threat to both the...
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Binary Peacock Algorithm: A Novel Metaheuristic Approach for Feature Selection
Binary metaheuristic algorithms prove to be invaluable for solving binary optimization problems. This paper proposes a binary variant of the peacock...
