Results 11 to 20 of about 803,970 (265)
Macrostate data clustering [PDF]
Physical Review E, 2003 We develop an effective nonhierarchical data clustering method using an analogy to the dynamic coarse graining of a stochastic system. Analyzing the eigensystem of an interitem transition matrix identifies fuzzy clusters corresponding to the metastable macroscopic states (macrostates) of a diffusive system.
Korenblum, Daniel, Shalloway, David
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Hypergraph-Regularized Lp Smooth Nonnegative Matrix Factorization for Data Representation
Mathematics, 2023 Nonnegative matrix factorization (NMF) has been shown to be a strong data representation technique, with applications in text mining, pattern recognition, image processing, clustering and other fields.
Yunxia Xu +3 more
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Applied Sciences, 2022 Cities are considered complex and open environments with multidimensional aspects including urban forms, urban imagery, and urban energy performance.
Chenyi Cai, Mohamed Zaghloul, Biao Li
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Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design
Sensors, 2023 In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capability in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different ...
Hugo Wai Leung Mak +2 more
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Modern Business Data Analysis and Data Visualization: A Real-Time Fusion Study [PDF]
ITM Web of Conferences, 2023 In contemporary data science and analytics, data clustering is a small bucket that divides computation among various child nodes. The network’s capacity, specialized tools, and applications that cannot be trained quickly are among these methods ...
Priya J Suji +3 more
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K-RBBSO Algorithm: A Result-Based Stochastic Search Algorithm in Big Data
Applied Sciences, 2022 Clustering is widely used in client-facing businesses to categorize their customer base and deliver personalized services. This study proposes an algorithm to stochastically search for an optimum solution based on the outcomes of a data clustering ...
Sungjin Park, Sangkyun Kim
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Clustering Data by Melting [PDF]
Neural Computation, 1993 We derive a new clustering algorithm based on information theory and statistical mechanics, which is the only algorithm that incorporates scale. It also introduces a new concept into clustering: cluster independence. The cluster centers correspond to the local minima of a thermodynamic free energy, which are identified as the fixed points of a one ...
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Clustering Transactional Data [PDF]
, 2002 In this paper we present a partitioning method capable to manage transactions, namelyt uples of variable size of categorical data. We adapt the standard definition of mathematical distance used in the KMeans algorithm to represent dissimilarityam ong transactions, and redefine the notion of cluster centroid.
Giannotti F, Gozzi C, Manco G
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Application of Multivariate-Rank-Based Techniques in Clustering of Big Data
Vikalpa, 2018 Executive Summary Very large or complex data sets, which are difficult to process or analyse using traditional data handling techniques, are usually referred to as big data.
Pritha Guha
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Proceedings of the 7th International Conference on Data Science, Technology and Applications, 2018 abstract
Michele Ianni +3 more
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