Results 271 to 280 of about 18,390,026 (306)
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Transforming Complex Problems Into K-Means Solutions
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023K-means is a fundamental clustering algorithm widely used in both academic and industrial applications. Its popularity can be attributed to its simplicity and efficiency.
Hongfu Liu +3 more
semanticscholar +1 more source
Microscopy research and technique (Print), 2021
Image processing plays a major role in neurologists' clinical diagnosis in the medical field. Several types of imagery are used for diagnostics, tumor segmentation, and classification.
A. Khan +5 more
semanticscholar +1 more source
Image processing plays a major role in neurologists' clinical diagnosis in the medical field. Several types of imagery are used for diagnostics, tumor segmentation, and classification.
A. Khan +5 more
semanticscholar +1 more source
Skin cancer detection from dermoscopic images using deep learning and fuzzy k‐means clustering
Microscopy research and technique (Print), 2021Melanoma skin cancer is the most life‐threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effectively ...
Marriam Nawaz +6 more
semanticscholar +1 more source
Dynamic Coverage Control Based on K-Means
IEEE transactions on industrial electronics (1982. Print), 2022In this article, we propose the dynamic coverage control method based on K-means. In the traditional coverage control, Voronoi partition method is used to assign the coverage positions for intelligent units. However, the Voronoi partition method requires
Ieee Dengxiu Yu Member +5 more
semanticscholar +1 more source
Fuzzy K-Means Clustering With Discriminative Embedding
IEEE Transactions on Knowledge and Data Engineering, 2022Fuzzy K-Means (FKM) clustering is of great importance for analyzing unlabeled data. FKM algorithms assign each data point to multiple clusters with some degree of certainty measured by the membership function.
F. Nie +4 more
semanticscholar +1 more source
Behavior Research Methods, 2013
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic,
Timmerman, Marieke E. +3 more
openaire +2 more sources
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic,
Timmerman, Marieke E. +3 more
openaire +2 more sources
A Unified Form of Fuzzy C-Means and K-Means algorithms and its Partitional Implementation
Knowledge-Based Systems, 2021This paper proposes as an element of novelty the Unified Form (UF) clustering algorithm, which treats Fuzzy C-Means (FCM) and K-Means (KM) algorithms as a single configurable algorithm.
I. Borlea +3 more
semanticscholar +1 more source
Metode K-Means Clustering Dalam Pengelompokan Penjualan Produk Frozen Food
Jurnal Ilmu Komputer dan Sistem InformasiAbstrak Terciptanya banyak bisnis di bidang penjualan berbasis online atau yang dikenal dengan istilah e-commerce menjadi bukti bahwa teknologi internet saat ini berkembang begitu pesat di berbagai industri, termasuk bisnis.
Lutfhia Azzahra +5 more
semanticscholar +1 more source
, 2020
Clustering is an explorative data analysis technique used for investigating the underlying structure in the data. It described as the grouping of objects, where the objects share similar characteristics. Over the past 50 years, clustering has been widely
P. Govender, V. Sivakumar
semanticscholar +1 more source
Clustering is an explorative data analysis technique used for investigating the underlying structure in the data. It described as the grouping of objects, where the objects share similar characteristics. Over the past 50 years, clustering has been widely
P. Govender, V. Sivakumar
semanticscholar +1 more source