New internal clustering validation measure for contiguous arbitrary‐shape clusters

Rojas-Thomas, Juan Carlos; Santos, Matilde

doi:10.1002/int.22521

Cited by 16 publications

(3 citation statements)

References 36 publications

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“…This is an important step because once singletons are formed, they cannot be merged to higher density clusters, just using Algorithm 1, since their intra-clonal distance a i is zero, and it is smaller than any other inter-clonal distance. Thus, we defined the Algorithm 2 that considers every singleton k, and tries to merge it to its closest neighbor l if the merging does not substantially decrease cluster uniformity [27]. More precisely, we merge k and l if |unif(l + k) − unif(l)| < δ.…”

Section: Mobillementioning

confidence: 99%

A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data

et al. 2022

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The adaptive B cell response is driven by the expansion, somatic hypermutation, and selection of B cell clonal lineages. A high number of clonal lineages in a B cell population indicates a highly diverse repertoire, while clonal size distribution and sequence diversity antigen selective pressure. Identifying clonal lineages is fundamental to many repertoire studies, including repertoire comparisons, clonal tracking, and statistical analysis. Several methods have been developed to group sequences from high-throughput B cell repertoire data. Current methods use clustering algorithms to group clonally-related sequences based on their similarities or distances. Such approaches create groups by optimizing a single objective that typically minimizes intra-clonal distances. However, optimizing several objective functions can be advantageous and boost the algorithm convergence rate. Here we propose a new method based on multi-objective clustering. Our approach requires V(D)J annotations to obtain the initial groups and iteratively applies two objective functions that optimize cohesion and separation within clonal lineages simultaneously. We show that our method greatly improves clonal lineage grouping on simulated benchmarks with varied mutation rates compared to other tools. When applied to experimental repertoires generated from high-throughput sequencing, its clustering results are comparable to the most performing tools and can reproduce the results of previous publications. The method based on multi-objective clustering can accurately identify clonally-related antibody sequences and presents the lowest running time among state-of-art tools. All these features constitute an attractive option for repertoire analysis, particularly in the clinical context. MobiLLe can potentially help unravel the mechanisms involved in developing and evolving B cell malignancies.

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Section: Mobillementioning

confidence: 99%

A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data

et al. 2022

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“…This involves not only finding the "true" number of clusters but also the optimal partition that best fits the underlying grouping structure of the data set. The cluster validity index (CVI) is an indicator, criteria or measure by which to provide a way of validating the quality of clustering algorithms in determining the correct true number of clusters in data sets (Rojas-Thomas & Santos, 2021). In general, CVIs tailored to quantitatively evaluate clustering results are classified into two groups: internal and external (Halkidi et al, 2001).…”

Section: Cluster Analysismentioning

confidence: 99%

Statistical and clustering validation analysis of primary students' learning outcomes and self-awareness of information and technical online security problems at a post-pandemic time

Panskyi

Korzeniewska

2022

Educ Inf Technol

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The authors decided to investigate the impact of the pandemic period and the resulting limitations in Polish primary school online security education. The first part of the study investigates the impact of the COVID-19 pandemic on students’ educational learning outcomes in information and Internet security. The study has been performed via a student-oriented survey of 20 questions. The statistical analysis confirms the significant difference before and after the pandemic in several questions at most. Nevertheless, this justifies the statement that pandemics had a positive impact on post-pandemic Internet-related security education. The second part of the study has been focused on students' perception and self-awareness of cyberspace problems. For this purpose, the authors used novel majority-based decision fusion clustering validation methods. The revealed results illustrate the positive tendency toward the students' self-awareness and self-confidence of online security problems and e-threats before, during and after the challenging pandemic period. Moreover, the presented validation methods show the appealing performance in educational data analysis, and therefore, the authors recommended these methods as a preprocessing step that helps to explore the intrinsic data structures or students' behaviors and as a postprocessing step to predict learning outcomes in different educational environments.

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“…Tese data are called multiview data [1][2][3][4][5]. Since multiview data typically ofers compatible and complementary information, it is more comprehensive for object description in comparison with the single-view data [6][7][8]. Since this beneft has garnered a lot of attention in recent years, numerous efcient multiview clustering techniques have been put forth to enhance clustering performance by integrating the information presented in diferent views.…”

Section: Introductionmentioning

confidence: 99%

Incomplete Multiview Clustering via Low‐Rank Tensor Ring Completion

Huang

Duan

et al. 2023

International Journal of Intelligent Systems

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Since real-world multiview data frequently contains numerous samples that are not observed from some viewpoints, the incomplete multiview clustering (IMC) issue has received a great deal of attention recently. However, most existing IMC methods choose to zero-fill the missing instances, which leads to the failure to exploit information hidden in the missing instances, and high-order interactions between various views. To tackle these problems, we proposed an effective IMC method using low-rank tensor ring completion, which was demonstrated to be powerful in exploiting high-order correlation. Specifically, we first stack the incomplete similarity graphs of all views into a 3rd-order incomplete tensor and then restore it via the tensor ring decomposition. Next, using an adaptive weighting technique, we apply multiview spectral clustering to all entire graphs in order to balance the contributions of different viewpoints and identify the consensus representation for grouping. Finally, we employ the alternating direction method of multipliers (ADMM) to optimize the suggested model. Numerous experimental findings on numerous different datasets show that the suggested approach is superior to other cutting-edge approaches.

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New internal clustering validation measure for contiguous arbitrary‐shape clusters

Cited by 16 publications

References 36 publications

A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data

A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data

Statistical and clustering validation analysis of primary students' learning outcomes and self-awareness of information and technical online security problems at a post-pandemic time

Incomplete Multiview Clustering via Low‐Rank Tensor Ring Completion

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