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A Benchmark Suite for Clustering Algorithms
, 2022 A Framework for Benchmarking Clustering Algorithms Benchmark Suite Version 1.0.1 How to Cite: Please cite the following paper which describes the overall benchmarking methodology: Gagolewski M., A Framework for Benchmarking Clustering Algorithms, 2022,
Gagolewski, Marek
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Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions
JISKA (Jurnal Informatika Sunan Kalijaga)The K-Means algorithm is a fundamental tool in machine learning, widely utilized for data clustering tasks. This research aims to improve the performance of the K-Means algorithm by integrating the Purity method, specifically focusing on clustering ...
Novia Hasdyna +2 more
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ACM Computing Surveys, 2011 In the last few years we have observed a proliferation of approaches for clustering XML documents and schemas based on their structure and content. The presence of such a huge amount of approaches is due to the different applications requiring the clustering of XML data.
Alsayed Algergawy +3 more
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Exploring Artificial Personality Grouping Through Decision Making in Feature Spaces
AIHuman personality (HP) is seen as an individual’s consistent patterns of feeling, thinking, and behaving by today’s psychological studies, in which HPs are characterized in terms of traits—in particular, as relatively enduring characteristics that ...
Yuan Zhou, Siamak Khatibi
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Privileged Information for Data Clustering [PDF]
Information Sciences, 2012 Many machine learning algorithms assume that all input samples are independently and identically distributed from some common distribution on either the input space X, in the case of unsupervised learning, or the input and output space X × Y in the case of supervised and semi-supervised learning.
Jan Feyereisl, Uwe Aickelin
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IEEE Access, 2021 The existing radio frequency-based positioning approaches widely used for indoor localization-based service (LBS) are fingerprinting and trilateration and their integration with the inertial sensors-based dead reckoning system.
Santosh Subedi +3 more
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Spectral clustering with imbalanced data [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 Spectral clustering (SC) and graph-based semi-supervised learning (SSL) algorithms are sensitive to how graphs are constructed from data. In particular if the data has proximal and unbalanced clusters these algorithms can lead to poor performance on well-known graphs such as $k$-NN, full-RBF, $ε$-graphs. This is because the objectives such as Ratio-Cut
Jing Qian, Venkatesh Saligrama
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Relapsed and/or refractory (R/R) pediatric acute leukemia carries a dismal prognosis, largely driven by chemoresistance to conventional salvage therapy. The BH3‐mimetic venetoclax, combined with chemotherapy or hypomethylating agents, has demonstrated efficacy in small clinical trials.
Katherine S. Colman +9 more
wiley +1 more source
Stochastic Numerical P Systems With Application in Data Clustering Problems
IEEE Access, 2020 This paper proposes an extension of numerical P (NP) systems, called stochastic numerical P (StNP) systems. In StNP systems, a novel stochastic production function-repartition protocol is developed to evolve the variables.
Jinyu Yang +3 more
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Identifying Patterns of Hospitalizations Among Childhood Cancer Survivors: A Population‐Based Study
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Childhood cancer survivors (CCSs) experience more hospitalizations than the general population. We aimed to identify distinct hospitalization patterns among Queensland CCSs and the associated characteristics. Methods Cases (aged ≤14 years) diagnosed between 1995 and 2019 while residents of Queensland were identified through the ...
Hai Pham +7 more
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