A New Method for Automatic Determining of the DBSCAN Parameters

Starczewski, Artur; Goetzen, Piotr; Er, Meng Joo

doi:10.2478/jaiscr-2020-0014

Cited by 54 publications

(31 citation statements)

References 28 publications

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“…A few more studies utilizing the DBSCAN algorithm in different fields of research can be found in very recent works [25][26][27][28]. On the other hand, importantly, the recent article [29] searches a way to determine the correct values of the DBSCAN parameters, by detecting the sharp increase in distance.…”

Section: Methodsmentioning

confidence: 99%

Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

HABİBOĞLU,

HERNANDEZ,

UYAVER

2022

Turkish Journal of Electrical Engineering and Computer Sciences

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Tyrosine, tryptophan, and phenylalanine are important aromatic amino acids for human health. If they are not properly metabolized, severe rare mental or metabolic diseases can emerge, many of which are not researched enough due to economic priorities. In our previous simulations, all three of these amino acids are discovered to be self-organizing and to have complex aggregations at different temperatures. Two of these essential stable formations are observed during our simulations: tubular-like and spherical-like structures. In this study, we develop and implement a clustering analyzing algorithm using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to measure the shapes of the formed structures by the self-assembly processes of these amino acids. We present the results in quantitative and qualitative ways. To the best of our knowledge, the geometric shapes of the formed structures by the self-assembly processes of these amino acids are not measured quantitatively in the literature. Analytical measurements and comparisons of these aggregations might help us to identify the self-aggregations quickly at early stages in our simulations and hence provide us with more opportunity to experiment with different parameters of the molecular simulations (like temperature, mixture rates, density etc.). We first implement DBSCAN method to identify the main self-aggregation cluster and then we develop and implement two algorithms to measure the shapes of the formed structures by the self-assembly processes of these amino acids. The measurements, which are completely in line with our simulation results, are presented in quantitative and qualitative ways.

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Section: Methodsmentioning

confidence: 99%

Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

HABİBOĞLU,

HERNANDEZ,

UYAVER

2022

Turkish Journal of Electrical Engineering and Computer Sciences

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“…However, Figure 7 shows there are two of those points. In that case, it is possible to choose one of them arbitrarily (Starczewski, Goetzen, & Joo Er, 2020). We chose the first inflection point in Figure 7, which corresponds to an eps = 2.87 for k = 2.…”

Section: Technique 3: Density-based Spatial Clustering and Applicatiomentioning

confidence: 99%

Cluster Analysis in Practice: Dealing with Outliers in Managerial Research

Lopes

Gosling

2021

Rev. adm. contemp.

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Context: in recent years, cluster analysis has stimulated researchers to explore new ways to understand data behavior. The computational ease of this method and its ability to generate consistent outputs, even in small datasets, explain that to some extent. However, researchers are often mistaken in holding that clustering is a terrain in which anything goes. The literature shows the opposite: they must be careful, especially regarding the effect of outliers on cluster formation. Objective: in this tutorial paper, we contribute to this discussion by presenting four clustering techniques and their respective advantages and disadvantages in the treatment of outliers. Methods: for that, we worked from a managerial dataset and analyzed it using k-means, PAM, DBSCAN, and FCM techniques. Results: our analyzes indicate that researchers have distinct clustering techniques for dealing with outliers accordingly. Conclusion: we concluded that researchers need to have a more diversified repertoire of clustering techniques. After all, this would give them two relevant empirical alternatives: choose the most appropriate technique for their research objectives or adopt a multi-method approach.

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“…However, the traditional DBSCAN algorithm works based on the user-defined global parameters ε and Minpts to characterize the internal structure of data sets, which fails in dealing with non-uniformly distributed data sets. The clustering parameters ε and Minpts are often user-specified based on experience or semi-automatically determined [31][32][33][34]. In this respect, a spatio-temporal zoning method considering non-uniform distribution of deformation information, named RDI-Mδ-DBSCAN method, is proposed by establishing zoning indicators considering deformation amplitude heterogeneity and improving the traditional DBSCAN algorithm with two new parameters M and δ.…”

Section: Introductionmentioning

confidence: 99%

A Spatio-Temporal Dam Deformation Zoning Method Considering Non-Uniform Distribution of Monitoring Information

Wang

Chen

et al. 2021

IEEE Access

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Deformation is the most intuitive indicator of the actual working status of a concrete dam. Zoning the variation regulation of dam deformation is one of the key parts of dam safety evaluation and risk assessment. However, the sample points reflecting deformation and variation characteristic information are non-uniformly distributed, thus it is difficult to cluster the data samples by traditional clustering methods. To solve this problem, a spatio-temporal zoning method of dam deformation considering non-uniform distribution of monitoring information is proposed. Firstly, the preprocessed deformation data are utilized to establish the similarity-distance zoning indicators using the absolute deformation, the deformation increase and the relative deformation increase respectively; then the deformation data are transferred into the Cartesian coordinate system, known as sample points. Secondly, utilize the improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster the points. The clustering parameters M and δ are determined by an optimization algorithm with an evaluation index as the objective function, then the sample points representing time sections or spatial monitoring points are clustered through dynamically updating the neighborhood radius value ε. Moreover, several artificial data sets are selected to demonstrate that the improved DBSCAN algorithm is with more obvious superiority in nonuniform clustering compared to traditional algorithms. Deformation data of an existing concrete dam are presented and discussed to validate the established zoning method.

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A New Method for Automatic Determining of the DBSCAN Parameters

Cited by 54 publications

References 28 publications

Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

Cluster Analysis in Practice: Dealing with Outliers in Managerial Research

A Spatio-Temporal Dam Deformation Zoning Method Considering Non-Uniform Distribution of Monitoring Information

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