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Dynamic Kernel Clustering by Spider Monkey Optimization Algorithm
In data, analysis clustering plays a major role. In the past decade varieties of clustering algorithms are proposed and produced better results. But...
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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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Entropy-based fuzzy clustering of interval-valued time series
This paper proposes a fuzzy C -medoids-based clustering method with entropy regularization to solve the issue of grou** complex data as...
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Clustering ensemble extraction: a knowledge reuse framework
Clustering ensemble combines several fundamental clusterings with a consensus function to produce the final clustering without gaining access to data...
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Clustering by deep latent position model with graph convolutional network
With the significant increase of interactions between individuals through numeric means, clustering of nodes in graphs has become a fundamental...
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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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Network and attribute-based clustering of tennis players and tournaments
This paper aims at targeting some relevant issues for clustering tennis players and tournaments: (i) it considers players, tournaments and the...
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Fuzzy clustering of time series based on weighted conditional higher moments
This paper proposes a new approach to fuzzy clustering of time series based on the dissimilarity among conditional higher moments. A system of...
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k-means clustering for persistent homology
Persistent homology is a methodology central to topological data analysis that extracts and summarizes the topological features within a dataset as a...
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A Survey on Model-Based Co-Clustering: High Dimension and Estimation Challenges
Model-based co-clustering can be seen as a particularly important extension of model-based clustering. It allows for a significant reduction of both...
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Sparse and smooth functional data clustering
A new model-based procedure is developed for sparse clustering of functional data that aims to classify a sample of curves into homogeneous groups...
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Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform
This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform...
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Logistic Normal Multinomial Factor Analyzers for Clustering Microbiome Data
The human microbiome plays an important role in human health and disease status. Next-generating sequencing technologies allow for quantifying the...
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Functional clustering of fictional narratives using Vonnegut curves
Motivated by a public suggestion by the famous novelist Kurt Vonnegut, we clustered functional data that represented sentiment curves for famous...
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Analysis of Recurrent Event Processes with Dynamic Models for Event Counts
Recurrent events are of interest in many research fields. The analysis of past developments of processes through dynamic covariates is useful to...
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Spatio-temporal clustering analysis using generalized lasso with an application to reveal the spread of Covid-19 cases in Japan
This study addressed the issue of determining multiple potential clusters with regularization approaches for the purpose of spatio-temporal...
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Clustering Financial Time Series by Dependency
In this paper, we propose a procedure for clustering financial time series by dependency on their volatilities. Our procedure is based on the...
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Chimeral Clustering
Hybrid species tend to exhibit a mixture of parent characteristics; we propose chimeral clusters as exhibiting a mixture of parent parameters, a type...
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State-Space Models for Clustering of Compositional Trajectories
Compositional data are drawing increasing interest for their ability to depict interdependent and constrained observations. While time series...