Analytical review of clustering techniques and proximity measures

Mehta, Vivek; Bawa, Seema; Singh, Jasmeet

doi:10.1007/s10462-020-09840-7

Cited by 34 publications

(20 citation statements)

References 51 publications

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“…As shown in Fig. 1, there are many specific algorithms in each category [26,[29][30][31][32]. Various clustering algorithms are introduced in detail in this section, including principles, advantages, and disadvantages.…”

Section: Clustering Technologiesmentioning

confidence: 99%

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

et al. 2023

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Clustering is an unsupervised learning technology, and it groups information (observations or datasets) according to similarity measures. Developing clustering algorithms is a hot topic in recent years, and this area develops rapidly with the increasing complexity of data and the volume of datasets. In this paper, the concept of clustering is introduced, and the clustering technologies are analyzed from traditional and modern perspectives. First, this paper summarizes the principles, advantages, and disadvantages of 20 traditional clustering algorithms and 4 modern algorithms. Then, the core elements of clustering are presented, such as similarity measures and evaluation index. Considering that data processing is often applied in vehicle engineering, finally, some specific applications of clustering algorithms in vehicles are listed and the future development of clustering in the era of big data is highlighted. The purpose of this review is to make a comprehensive survey that helps readers learn various clustering algorithms and choose the appropriate methods to use, especially in vehicles.

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Section: Clustering Technologiesmentioning

confidence: 99%

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

et al. 2023

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“…To remove the multiple-scale problem, features are normalized prior to clustering or non-linear distance metrics with saturation limits can be used. Some metrics that are commonly used in clustering are the mahalanobis distance

, cosine similarity

, or Pearson correlation [ 173 ].…”

Section: Selected Applications Of Machine Learning In Computational B...mentioning

confidence: 99%

Protein Function Analysis through Machine Learning

et al. 2022

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Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

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“…Cluster analysis is a kind of statistical analysis method guiding the process that splits a set of data into some clusters [24,36,37,65], where the data in the same clusters have more similar characteristic than those in the different clusters. It is an unsupervised process, which is very different from the classification analysis [41].…”

Section: Case Study: Cluster Analysismentioning

confidence: 99%

Directional correlation coefficient measures for Pythagorean fuzzy sets: their applications to medical diagnosis and cluster analysis

Lin

Huang

Chen

et al. 2021

Complex Intell. Syst.

119

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Compared to the intuitionistic fuzzy sets, the Pythagorean fuzzy sets (PFSs) can provide the decision makers with more freedom to express their evaluation information. There exist some research results on the correlation coefficient between PFSs, but sometimes they fail to deal with the problems of disease diagnosis and cluster analysis. To tackle the drawbacks of the existing correlation coefficients between PFSs, some novel directional correlation coefficients are put forward to compute the relationship between two PFSs by taking four parameters of the PFSs into consideration, which are the membership degree, non-membership degree, strength of commitment, and direction of commitment. Afterwards, two practical examples are given to show the application of the proposed directional correlation coefficient in the disease diagnosis, and the application of the proposed weighted directional correlation coefficient in the cluster analysis. Finally, they are compared with the previous correlation coefficients that have been developed for PFSs.

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Analytical review of clustering techniques and proximity measures

Cited by 34 publications

References 51 publications

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

Protein Function Analysis through Machine Learning

Directional correlation coefficient measures for Pythagorean fuzzy sets: their applications to medical diagnosis and cluster analysis

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