scholarly journals Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1763
Author(s):  
Miroslava Nedyalkova ◽  
Costel Sarbu ◽  
Marek Tobiszewski ◽  
Vasil Simeonov
Keyword(s):  
Physicochemical Properties ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Simple Procedure ◽  
Hybrid Approach ◽  
Molecular Symmetry ◽  
Clustering Methods ◽  
Fuzzy Partition ◽  
Large Group ◽  
Divisive Hierarchical Clustering

The present study describes a simple procedure to separate into patterns of similarity a large group of solvents, 259 in total, presented by 15 specific descriptors (experimentally found and theoretically predicted physicochemical parameters). Solvent data is usually characterized by its high variability, different molecular symmetry, and spatial orientation. Methods of chemometrics can usefully be used to extract and explore accurately the information contained in such data. In this order, advanced fuzzy divisive hierarchical-clustering methods were efficiently applied in the present study of a large group of solvents using specific descriptors. The fuzzy divisive hierarchical associative-clustering algorithm provides not only a fuzzy partition of the solvents investigated, but also a fuzzy partition of descriptors considered. In this way, it is possible to identify the most specific descriptors (in terms of higher, smallest, or intermediate values) to each fuzzy partition (group) of solvents. Additionally, the partitioning performed could be interpreted with respect to the molecular symmetry. The chemometric approach used for this goal is fuzzy c-means method being a semi-supervised clustering procedure. The advantage of such a clustering process is the opportunity to achieve separation of the solvents into similarity patterns with a certain degree of membership of each solvent to a certain pattern, as well as to consider possible membership of the same object (solvent) in another cluster. Partitioning based on a hybrid approach of the theoretical molecular descriptors and experimentally obtained ones permits a more straightforward separation into groups of similarity and acceptable interpretation. It was shown that an important link between objects’ groups of similarity and similarity groups of variables is achieved. Ten classes of solvents are interpreted depending on their specific descriptors, as one of the classes includes a single object and could be interpreted as an outlier. Setting the results of this research into broader perspective, it has been shown that the fuzzy clustering approach provides a useful tool for partitioning by the variables related to the main physicochemical properties of the solvents. It gets possible to offer a simple guide for solvents recognition based on theoretically calculated or experimentally found descriptors related to the physicochemical properties of the solvents.

scholarly journals A Hybrid Deep Clustering Approach for Robust Cell Type Profiling Using Single-cell RNA-seq Data

2019 ◽  
Author(s):  
Suhas Srinivasan ◽  
Nathan T. Johnson ◽  
Dmitry Korkin
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Hybrid Approach ◽  
Feature Learning ◽  
Specific Cell ◽  
Clustering Methods ◽  
Model Based Clustering ◽  
Clustering And Classification ◽  
Living Organisms

AbstractSingle-cell RNA sequencing (scRNA-seq) is a recent technology that enables fine-grained discovery of cellular subtypes and specific cell states. It routinely uses machine learning methods, such as feature learning, clustering, and classification, to assist in uncovering novel information from scRNA-seq data. However, current methods are not well suited to deal with the substantial amounts of noise that is created by the experiments or the variation that occurs due to differences in the cells of the same type. Here, we develop a new hybrid approach, Deep Unsupervised Single-cell Clustering (DUSC), that integrates feature generation based on a deep learning architecture with a model-based clustering algorithm, to find a compact and informative representation of the single-cell transcriptomic data generating robust clusters. We also include a technique to estimate an efficient number of latent features in the deep learning model. Our method outperforms both classical and state-of-the-art feature learning and clustering methods, approaching the accuracy of supervised learning. The method is freely available to the community and will hopefully facilitate our understanding of the cellular atlas of living organisms as well as provide the means to improve patient diagnostics and treatment.

Research on NMF Based Hierarchical Clustering Methods

2010 ◽  
Vol 439-440 ◽  
pp. 1306-1311
Author(s):  
Fang Li ◽  
Qun Xiong Zhu
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Clustering Method ◽  
Hierarchical Agglomerative Clustering ◽  
Hierarchical Clustering Methods

LSI based hierarchical agglomerative clustering algorithm is studied. Aiming to the problems of LSI based hierarchical agglomerative clustering method, NMF based hierarchical clustering method is proposed and analyzed. Two ways of implementing NMF based method are introduced. Finally the result of two groups of experiment based on the TanCorp document corpora show that the method proposed is effective.

scholarly journals Hierarchical kt jet clustering for parallel architectures

2017 ◽  
Vol 9 (2) ◽  
pp. 195-213
Author(s):  
Richárd Forster ◽  
Ágnes Fülöp
Keyword(s):  
Hierarchical Clustering ◽  
Particle Physics ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
High Energy ◽  
Theoretical Physics ◽  
High Energy Particle ◽  
Clustering Methods ◽  
Hierarchical Clustering Methods ◽  
Using Data

AbstractThe reconstruction and analyze of measured data play important role in the research of high energy particle physics. This leads to new results in both experimental and theoretical physics. This requires algorithm improvements and high computer capacity. Clustering algorithm makes it possible to get to know the jet structure more accurately. More granular parallelization of the kt cluster algorithms was explored by combining it with the hierarchical clustering methods used in network evaluations. The kt method allows to know the development of particles due to the collision of high-energy nucleus-nucleus. The hierarchical clustering algorithms works on graphs, so the particle information used by the standard kt algorithm was first transformed into an appropriate graph, representing the network of particles. Testing was done using data samples from the Alice offine library, which contains the required modules to simulate the ALICE detector that is a dedicated Pb-Pb detector. The proposed algorithm was compared to the FastJet toolkit's standard longitudinal invariant kt implementation. Parallelizing the standard non-optimized version of this algorithm utilizing the available CPU architecture proved to be 1:6 times faster, than the standard implementation, while the proposed solution in this paper was able to achieve a 12 times faster computing performance, also being scalable enough to efficiently run on GPUs.

Finding specific peaks (markers) using fuzzy divisive hierarchical associative-clustering based on the chromatographic profiles of medicinal plant extracts obtained at various detection wavelengths

2020 ◽  
Vol 12 (25) ◽  
pp. 3260-3267
Author(s):  
Ileana M. Simion ◽  
Augustin-C. Moţ ◽  
Costel Sârbu
Keyword(s):  
Medicinal Plant ◽  
Hierarchical Clustering ◽  
Plant Extracts ◽  
Clustering Algorithm ◽  
Chemometric Methods ◽  
Fuzzy C Means ◽  
Divisive Hierarchical Clustering ◽  
Medicinal Plant Extracts ◽  
Hierarchical Clustering Algorithm ◽  
Chromatographic Profiles

Advanced chemometric methods, such as fuzzy c-means (FCM), a fuzzy divisive hierarchical clustering algorithm (FDHC), and fuzzy divisive hierarchical associative-clustering (FDHAC), have been successfully applied in this study.

scholarly journals A Novel Local Density Hierarchical Clustering Algorithm Based on Reverse Nearest Neighbors

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Yaohui Liu ◽  
Dong Liu ◽  
Fang Yu ◽  
Zhengming Ma
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Local Density ◽  
Clustering Algorithms ◽  
Real Data ◽  
Nearest Neighbors ◽  
Clustering Methods ◽  
Density Peak ◽  
Hierarchical Clustering Algorithm

Clustering is widely used in data analysis, and density-based methods are developed rapidly in the recent 10 years. Although the state-of-art density peak clustering algorithms are efficient and can detect arbitrary shape clusters, they are nonsphere type of centroid-based methods essentially. In this paper, a novel local density hierarchical clustering algorithm based on reverse nearest neighbors, RNN-LDH, is proposed. By constructing and using a reverse nearest neighbor graph, the extended core regions are found out as initial clusters. Then, a new local density metric is defined to calculate the density of each object; meanwhile, the density hierarchical relationships among the objects are built according to their densities and neighbor relations. Finally, each unclustered object is classified to one of the initial clusters or noise. Results of experiments on synthetic and real data sets show that RNN-LDH outperforms the current clustering methods based on density peak or reverse nearest neighbors.

H-CLAP: hierarchical clustering within a linear array with an application in genetics

2015 ◽  
Vol 14 (2) ◽  
Author(s):  
Samiran Ghosh ◽  
Jeffrey P. Townsend
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Linear Array ◽  
Recursive Partitioning ◽  
Biological Significance ◽  
Data Sets ◽  
Clustering Methods ◽  
Domain Specific ◽  
Data Points ◽  
Fully Bayesian

AbstractIn most cases where clustering of data is desirable, the underlying data distribution to be clustered is unconstrained. However clustering of site types in a discretely structured linear array, as is often desired in studies of linear sequences such as DNA, RNA or proteins, represents a problem where data points are not necessarily exchangeable and are directionally constrained within the array. Each position in the linear array is fixed, and could be either “marked” (i.e., of interest such as polymorphic or substitute sites) or “non-marked.” Here we describe a method for clustering of those marked sites. Since the cluster-generating process is constrained by discrete locality inside such an array, traditional clustering methods need adjustment to be appropriate. We develop a hierarchical Bayesian approach. We adopt a Markov clustering algorithm, revealing any natural partitioning in the pattern of marked sites. The resulting recursive partitioning and clustering algorithm is named hierarchical clustering in a linear array (H-CLAP). It employs domain-specific directional constraints directly in the likelihood construction. Our method, being fully Bayesian, is more flexible in cluster discovery compared to a standard agglomerative hierarchical clustering algorithm. It not only provides hierarchical clustering, but also cluster boundaries, which may have their own biological significance. We have tested the efficacy of our method on data sets, including two biological and several simulated ones.

scholarly journals Rebalancing Strategy for Bike-Sharing Systems Based on the Model of Level of Detail

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhenghua Hu ◽  
Kejie Huang ◽  
Enyou Zhang ◽  
Qi’ang Ge ◽  
Xiaoxue Yang
Keyword(s):  
Genetic Algorithm ◽  
Hierarchical Clustering ◽  
Traditional Method ◽  
Clustering Algorithm ◽  
Level Of Detail ◽  
Daily Lives ◽  
Effective Time ◽  
Divisive Hierarchical Clustering ◽  
Bike Sharing ◽  
Hierarchical Clustering Algorithm

Traveling by bike-sharing systems has become an indispensable means of transportation in our daily lives because green commuting has gradually become a consensus and conscious action. However, the problem of “difficult to rent or to return a bike” has gradually become an issue in operating the bike-sharing system. Moreover, scientific and systematic schemes that can efficiently complete the task of rebalancing bike-sharing systems are lacking. This study aims to introduce the basic idea of the k-divisive hierarchical clustering algorithm. A rebalancing strategy based on the model of level of detail in combination with genetic algorithm was proposed. Data were collected from the bike-sharing system in Ningbo. Results showed that the proposed algorithm could alleviate the problem of the uneven distribution of the demand for renting or returning bikes and effectively improve the service from the bike-sharing system. Compared with the traditional method, this algorithm helps reduce the effective time for rebalancing bike-sharing systems by 28.3%. Therefore, it is an effective rebalancing scheme.

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