Improving a Centroid-Based Clustering by Using Suitable Centroids from Another Clustering
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DOI: 10.1007/s00357-018-9296-4
Abstract: Fast centroid-based clustering algorithms such as k-means usually converge to a local optimum. In this work, we propose a method for constructing a better clustering from two such suboptimal clustering solutions based on the fact… read more here.
Keywords: based clustering; using suitable; improving centroid; clustering using ... See more keywords
Fuzzy clustering using multiple Gaussian kernels with optimized-parameters
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DOI: 10.1007/s10700-017-9268-x
Abstract: In this paper, we propose a new kernel-based fuzzy clustering algorithm which tries to find the best clustering results using optimal parameters of each kernel in each cluster. It is known that data with nonlinear… read more here.
Keywords: fuzzy clustering; kernel based; based fuzzy; single kernel ... See more keywords
Density peaks clustering using geodesic distances
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DOI: 10.1007/s13042-017-0648-x
Abstract: Density peaks clustering (DPC) algorithm is a novel clustering algorithm based on density. It needs neither iterative process nor more parameters. However, it cannot effectively group data with arbitrary shapes, or multi-manifold structures. To handle… read more here.
Keywords: using geodesic; density; peaks clustering; geodesic distances ... See more keywords
F1-ECAC: Enhanced Evolutionary Clustering Using an Ensemble of Supervised Classifiers
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DOI: 10.1109/access.2021.3116092
Abstract: Clustering is an unsupervised learning technique used in data mining for finding groups with increased object similarity within but not between them. However, the absence of a-priori knowledge on the optimal clustering criterion, and the… read more here.
Keywords: enhanced evolutionary; evolutionary clustering; ecac enhanced; clustering using ... See more keywords
Enhanced Approach for Agglomerative Clustering Using Topological Relations
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DOI: 10.1109/access.2023.3252374
Abstract: Spatial data clustering has long been used to facilitate the knowledge discovery process. Several approaches have been proposed in the literature for detecting and understanding hidden patterns. These approaches are based on different perspectives and… read more here.
Keywords: topological relations; clustering using; enhanced approach; agglomerative clustering ... See more keywords
Clustering using unsupervised machine learning to stratify the risk of immune‐related liver injury
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DOI: 10.1111/jgh.16038
Abstract: Immune‐related liver injury (liver‐irAE) is a clinical problem with a potentially poor prognosis. read more here.
Keywords: immune related; liver injury; related liver; clustering using ... See more keywords
Clustering using kernel entropy principal component analysis and variable kernel estimator
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Electrical and Computer Engineering"
DOI: 10.11591/ijece.v11i3.pp2109-2119
Abstract: Clustering as unsupervised learning method is the mission of dividing data objects into clusters with common characteristics. In the present paper, we introduce an enhanced technique of the existing EPCA data transformation method. Incorporating the… read more here.
Keywords: kernel entropy; entropy; using kernel; entropy principal ... See more keywords
Grid-Based Clustering Using Boundary Detection
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DOI: 10.3390/e24111606
Abstract: Clustering can be divided into five categories: partitioning, hierarchical, model-based, density-based, and grid-based algorithms. Among them, grid-based clustering is highly efficient in handling spatial data. However, the traditional grid-based clustering algorithms still face many problems:… read more here.
Keywords: clustering using; boundary detection; based clustering; grid based ... See more keywords