DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data
Sign Up to like & getrecommendations! Published in 2018 at "Cytometry Part A"
DOI: 10.1002/cyto.a.23371
Abstract: Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional… read more here.
Keywords: cell; cell populations; dafi; data clustering ... See more keywords
Geospatial Data Clustering in Network Space: A Survey
Sign Up to like & getrecommendations! Published in 2025 at "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"
DOI: 10.1002/widm.70023
Abstract: Geospatial data enhances traditional datasets by integrating spatial and temporal dimensions, facilitating advanced visualizations and comprehensive analytical insights. As a fundamental aspect of geospatial analytics, geospatial data clustering (GDC) has become a prominent area of… read more here.
Keywords: clustering network; geospatial data; network space; data clustering ... See more keywords
Adaptive local learning regularized nonnegative matrix factorization for data clustering
Sign Up to like & getrecommendations! Published in 2018 at "Applied Intelligence"
DOI: 10.1007/s10489-018-1380-2
Abstract: Data clustering aims to group the input data instances into certain clusters according to the high similarity to each other, and it could be regarded as a fundamental and essential immediate or intermediate task that… read more here.
Keywords: adaptive local; matrix; data clustering; matrix factorization ... See more keywords
Hybrid metaheuristic algorithm for improving the efficiency of data clustering
Sign Up to like & getrecommendations! Published in 2018 at "Cluster Computing"
DOI: 10.1007/s10586-018-2242-8
Abstract: Clustering is a technique which is used to group the data into different subgroups or subsets to retrieve meaningful information from the available huge dataset. The trending swarm based intelligent system replaces the conventional clustering… read more here.
Keywords: metaheuristic algorithm; algorithm; algorithm improving; hybrid metaheuristic ... See more keywords
Using data clustering to reveal trainees’ behavior in cybersecurity education
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DOI: 10.1007/s10639-024-12480-x
Abstract: In cyber security education, hands-on training is a common type of exercise to help raise awareness and competence, and improve students’ cybersecurity skills. To be able to measure the impact of the design of the… read more here.
Keywords: education; tool; clustering reveal; data clustering ... See more keywords
Towards effective discovery of natural communities in complex networks and implications in e-commerce
Sign Up to like & getrecommendations! Published in 2020 at "Electronic Commerce Research"
DOI: 10.1007/s10660-019-09395-y
Abstract: Automated community detection is an important problem in the study of complex networks. The idea of community detection is closely related to the concept of data clustering in pattern recognition. Data clustering refers to the… read more here.
Keywords: communities complex; community detection; data clustering; complex networks ... See more keywords
Uncertain Data Clustering Based on Probability Distribution in Obstacle Space
Sign Up to like & getrecommendations! Published in 2020 at "Wireless Personal Communications"
DOI: 10.1007/s11277-019-06980-0
Abstract: In the case of current technology, most of the measurements are focused on geometric distance, and the distribution of data is not considered. In order to compensate for this shortcoming of geometric distance measurement, this… read more here.
Keywords: algorithm; obstacle space; uncertain data; data clustering ... See more keywords
A Distributed Neighbourhood DBSCAN Algorithm for Effective Data Clustering in Wireless Sensor Networks
Sign Up to like & getrecommendations! Published in 2021 at "Wireless Personal Communications"
DOI: 10.1007/s11277-021-08836-y
Abstract: Conventional K-Means based distributed data clustering has limitation of detecting arbitrary shape clusters and requires number of clusters a priori. To alleviate these issues in this paper, a Distributed Neighborhood DBSCAN (DN-DBSCAN) algorithm is introduced… read more here.
Keywords: distributed neighbourhood; dbscan algorithm; data clustering; sensor ... See more keywords
Visual topic models for healthcare data clustering
Sign Up to like & getrecommendations! Published in 2021 at "Evolutionary Intelligence"
DOI: 10.1007/s12065-019-00300-y
Abstract: Social media is a great source to search health-related topics for envisages solutions towards healthcare. Topic models originated from Natural Language Processing that is receiving much attention in healthcare areas because of interpretability and its… read more here.
Keywords: visual topic; topic models; healthcare data; data clustering ... See more keywords
A modified unconscious search algorithm for data clustering
Sign Up to like & getrecommendations! Published in 2021 at "Evolutionary Intelligence"
DOI: 10.1007/s12065-021-00578-x
Abstract: Clustering is a widely used data mining technique with a diverse set of applications. Since clustering is an NP-hard problem, finding high-quality solutions for large-scale clustering problems can be an arduous and computationally expensive task.… read more here.
Keywords: unconscious search; modified unconscious; data clustering; search ... See more keywords
A random version of principal component analysis in data clustering
Sign Up to like & getrecommendations! Published in 2018 at "Computational biology and chemistry"
DOI: 10.1016/j.compbiolchem.2018.01.009
Abstract: Principal component analysis (PCA) is a widespread technique for data analysis that relies on the covariance/correlation matrix of the analyzed data. However, to properly work with high-dimensional data sets, PCA poses severe mathematical constraints on… read more here.
Keywords: principal component; analysis; correlation matrix; data clustering ... See more keywords