Cluster Analysis

Odell, Patrick L.; Duran, Benjamin S.

doi:10.1007/978-3-642-46309-9

Cited by 17 publications

(9 citation statements)

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“…Duran and Odell [12] state that if d (Xi, Cj) gets smaller, the similarity between the two observation units is getting closer. The requirement to use Euclid distance is if all the features in the dataset are not correlated with each other.…”

Section: Literature Reviewmentioning

confidence: 99%

Identify the Severity of Cirrhosis Based on the Level of AST and ALT Enzymes Using Image Processing Approach and K-Means Clustering Method

Arianti¹,

Muda²,

Ahmad³

et al. 2020

Selected Papers From the 1st International Conference on Islam, Science and Technology, ICONISTECH-1 2019, 11-12 July 2019, Ban

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Section: Literature Reviewmentioning

confidence: 99%

Identify the Severity of Cirrhosis Based on the Level of AST and ALT Enzymes Using Image Processing Approach and K-Means Clustering Method

Arianti¹,

Muda²,

Ahmad³

et al. 2020

Selected Papers From the 1st International Conference on Islam, Science and Technology, ICONISTECH-1 2019, 11-12 July 2019, Ban

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“…Пусть, к примеру, r = 2 и рассмотрим случай, когда ISSN 2226-3780 один или оба ОПТ считают, что конкурент будет вести себя как дуополист Курно (предполагаем, что w k = ∞, k r = 1 2 , , ..., ). Предположим, что первый ОПТ считает, что второй ОПТ будет реагировать согласно зависимости Курно (10), (11), т. е.: Тогда результаты для обоих операторов будут зависеть от поведения второго ОПТ. Если второй ОПТ, по предположению первого ОПТ, пользуется реакцией Курно, то равновесие Стэкельберга для первого оператора дается соотношениями (15), (17):…”

Section: результаты исследования равновесных решений олигополииunclassified

“…В общем случае можно рассматривать конкуренцию между различными картелями, которые образуют некоторые ОПТ в целях максимизации общей прибыли картеля за перевалку груза. Общее число возможных картелей N равно сумме чисел возможных разбиений множества из r ОПТ на m r ≤ непересекающихся подмножеств, причем [11]: В такой рыночной структуре каждый картель будет стремиться максимизировать свою общую прибыль при условиях (5), (6). Здесь также можно рассматривать условия равновесия по Курно, Стэкельбергу и другим, но только применительно к отдельным картелям.…”

Section: результаты исследования равновесных решений олигополииunclassified

Компьютерные Технологии В Экономике

Мельников¹

2012

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“…A typical way of dealing with such large data sets is to bin data into groups based on some similarity metric. − For example, clustering methods take a set of samples (frames in the MD context) each with some number of features (atomic coordinates, dihedral angles, etc.) and assigns a label (cluster number) to each sample such that samples with the same label are similar while samples with different labels are dissimilar.…”

Section: Introductionmentioning

confidence: 99%

Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge

Melvin

Godwin

Xiao

et al. 2016

J. Chem. Theory Comput.

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As the length of molecular dynamics (MD) trajectories grows with increasing computational power, so does the importance of clustering methods for partitioning trajectories into conformational bins. Of the methods available, the vast majority require users to either have some a priori knowledge about the system to be clustered or to tune clustering parameters through trial and error. Here we present non-parametric uses of two modern clustering techniques suitable for first-pass investigation of an MD trajectory. Being non-parametric, these methods require neither prior knowledge nor parameter tuning. The first method, HDBSCAN, is fast—relative to other popular clustering methods—and is able to group unstructured or intrinsically disordered systems (such as intrinsically disordered proteins, or IDPs) into bins that represent global conformational shifts. HDBSCAN is also useful for determining the overall stability of a system—as it tends to group stable systems into one or two bins—and identifying transition events between metastable states. The second method, iMWK-Means, with explicit rescaling followed by K-Means, while slower than HDBSCAN, performs well with stable, structured systems such as folded proteins and is able to identify higher resolution details such as changes in relative position of secondary structural elements. Used in conjunction, these clustering methods allow a user to discern quickly and without prior knowledge the stability of a simulated system and identify both local and global conformational changes.

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Cluster Analysis

Cited by 17 publications

References 0 publications

Identify the Severity of Cirrhosis Based on the Level of AST and ALT Enzymes Using Image Processing Approach and K-Means Clustering Method

Identify the Severity of Cirrhosis Based on the Level of AST and ALT Enzymes Using Image Processing Approach and K-Means Clustering Method

Компьютерные Технологии В Экономике

Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge

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