Πλοήγηση ανά Θέμα "Clustering algorithms"
Αποτελέσματα 1-20 από 41
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Application of data mining-based affinity propagation clustering algorithm for diagnosis of mechanical equipment transmission system
(2020)– Data mining techniques used to monitor and diagnose the faults of the transmission system of mechanical equipment, thereby promoting the development of big data analysis in the field of intelligent diagnosis. The Affinity ... -
Approximate kNN Classification for Biomedical Data
(2020)We are in the era where the Big Data analytics has changed the way of interpreting the various biomedical phenomena, and as the generated data increase, the need for new machine learning methods to handle this evolution ... -
Bayesian uncertainty quantification and propagation in molecular dynamics simulations
(2012)A comprehensive Bayesian probabilistic framework is developed for quantifying and calibrating the uncertainties in the parameters of the models (e.g. force-field potentials) involved in molecular dynamics (MD) simulations ... -
Biomedical Data Ensemble Classification using Random Projections
(2019)Biomedicine is undergoing a revolution driven by the explosion of biomedical data, which are generated by emerged medical imaging, sensor technologies and high-throughput technologies. An indicative example is the single ... -
Classification of apoptosis using advanced clustering techniques on digital microscopic images
(2010)Programmed cell death, also known as apoptosis is of fundamental importance in many biological processes and also highly associated with serious diseases like cancer and HIV. The current paper presents an innovative method ... -
Clustering in Urban environments: Virtual forces applied to vehicles
(2013)Clustering of Vanets is a technique for grouping nodes in geographical vicinity together, making the network more robust and scalable. Clustering of vehicles that is based on virtual forces has been recently introduced for ... -
Clustering of high dimensional data streams
(2012)Clustering of data streams has become a task of great interest in the recent years as such data formats is are becoming increasingly ambiguous. In many cases, these data are also high dimensional and in result more complex ... -
Clustering of Urban Road Paths; Identifying the Optimal Set of Linear and Nonlinear Clustering Features
(2021)Urban traffic is undoubtedly a dynamic phenomenon presenting variations over both time and space, that in the majority of cases are the result of a mixture of, either well known (i.e. weather, seasonality) or not easily ... -
A combined clustering/symbolic regression framework for fluid property prediction
(2022)Symbolic regression techniques are constantly gaining ground in materials informatics as the machine learning counterpart capable of providing analytical equations exclusively derived from data. When the feature space is ... -
Confronting Sparseness and High Dimensionality in Short Text Clustering via Feature Vector Projections
(2020)Short text clustering is a popular problem that focuses on the unsupervised grouping of similar short text documents, or entitled entities. Since the short texts are currently being utilized in a vast number of applications, ... -
Convolutional Variational Autoencoders for Image Clustering
(2021)The problem of data clustering is one of the most fundamental and well studied problems of unsupervised learning. Image clustering, refers to one of the most challenging specifications of clustering, concerning image data. ... -
Distributed clustering in vehicular networks
(2012)Clustering in vanets is of crucial importance in order to cope with the dynamic features of the vehicular topologies. Algorithms that give good results in Manets fail to create stable clusters since vehicular nodes are ... -
Distributed Localized Contextual Event Reasoning under Uncertainty
(2017)We focus on Internet of Things (IoT) environments where sensing and computing devices (nodes) are responsible to observe, reason, report, and react to a specific phenomenon. Each node (e.g., an unmanned vehicle or an ... -
Efficient change detection for high dimensional data streams
(2015)The recent technological advancements in cloud computing and the access in increasing computational power has led in undertaking the data processing derived by mobile devices. In particular, when these data are high ... -
Enhancing Clustering of Single-Cell RNA-Seq Data by Proximity Learning on Random Projected Spaces
(2019)We are in the era of single-cell RNA sequencing technology, which offers a great potential for uncovering cellular differences with a higher resolution, shedding light in various complex biological processes and complex ... -
Evolutionary principal direction divisive partitioning
(2010)While data clustering has a long history and a large amount of research has been devoted to the development of clustering algorithms, significant challenges still remain. One of the most important challenges in the field ... -
Explainable Classification of Weakly Annotated Wireless Capsule Endoscopy Images Based on a Fuzzy Bag-of-Colour Features Model and Brain Storm Optimization
(2021)Wireless capsule endoscopy (WCE) constitutes a medical imaging technology developed for the endoscopic exploration of the gastrointestinal (GI) tract, whereas it provides a more comfortable examination method, in comparison ... -
Exploiting morphology and texture of 3D tumor models in DTI for differentiating glioblastoma multiforme from solitary metastasis
(2018)Ambiguous imaging appearance of Glioblastoma Multiforme (GBM) and solitary Metastasis (MET) is a challenge to conventional Magnetic Resonance Imaging (MRI) based diagnosis, leading to exploitation of advanced MRI techniques, ... -
Exploring patterns in water consumption by clustering
(2015)Water scarcity, high water demand due to increasing urbanization and the ongoing liberalization of the water and energy markets makes water utilities look into innovative ways to approach consumers, to offer attractive ... -
Extraction of Structural Regularity for Random Logic Netlists
(2019)We present two regularity extraction algorithms, Greedy and Isomorphism, which extract Structured DataPath (SDP) clusters from gate-level netlists, and an SDP placement algorithm, compatible with industrial tools. Greedy ...