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Sasha Petrenko, Daniel B. Hier, Mary A. Bone, Tayo Obafemi-Ajayi, Erik J. Timpson, William E. Marsh, Michael Speight and Donald C. Wunsch II
Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biologica...
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Konstantinos Gratsos , Stefanos Ougiaroglou and Dionisis Margaris
Partition-based clustering is widely applied over diverse domains. Researchers and practitioners from various scientific disciplines engage with partition-based algorithms relying on specialized software or programming libraries. Addressing the need to b...
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Changro Lee
Pág. 30 - 40
Although clustering analysis is a popular tool in unsupervised learning, it is inefficient for the datasets dominated by categorical variables, e.g., real estate datasets. To apply clustering analysis to real estate datasets, this study proposes an entit...
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Usman Sammani Sani, Owais Ahmed Malik and Daphne Teck Ching Lai
Wireless network parameters such as transmitting power, antenna height, and cell radius are determined based on predicted path loss. The prediction is carried out using empirical or deterministic models. Deterministic models provide accurate predictions ...
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Ali Seman and Azizian Mohd Sapawi
In the conventional k-means framework, seeding is the first step toward optimization before the objects are clustered. In random seeding, two main issues arise: the clustering results may be less than optimal and different clustering results may be obtai...
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Xia Que, Siyuan Jiang, Jiaoyun Yang and Ning An
Many mixed datasets with both numerical and categorical attributes have been collected in various fields, including medicine, biology, etc. Designing appropriate similarity measurements plays an important role in clustering these datasets. Many tradition...
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Junfang Gong, Jay Lee, Shunping Zhou and Shengwen Li
Human activity events are often recorded with their geographic locations and temporal stamps, which form spatial patterns of the events during individual time periods. Temporal attributes of these events help us understand the evolution of spatial proces...
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Emilie Tew-Kai, Victor Quilfen, Marie Cachera and Martial Boutet
In the context of maritime spatial planning and the implementation of spatialized Good Environmental Status indicators in the Marine Strategy Framework Directive (MSFD), the definition of a mosaic composed of coherent and standardised spatial units is ne...
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Rodrigo L. Rose, Tejas G. Puranik and Dimitri N. Mavris
The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have grown i...
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Athanasios Davvetas, Iraklis A. Klampanos, Spiros Skiadopoulos and Vangelis Karkaletsis
Evidence transfer for clustering is a deep learning method that manipulates the latent representations of an autoencoder according to external categorical evidence with the effect of improving a clustering outcome. Evidence transfer?s application on clus...
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