Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering

Hämäläinen, Joonas; Jauhiainen, Susanne; Kärkkäinen, Tommi

doi:10.3390/a10030105

Cited by 85 publications

(62 citation statements)

References 40 publications

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“…The term cluster validation is used to evaluate the performance of the clustering method and it helps us to choose a good clustering method among clustering methods [22]. In general, clustering validation can be labeled into three classes: Internal cluster validation, External cluster validation, and Relative cluster validation [5].…”

Section: Outputmentioning

confidence: 99%

WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis

Ibrahim

2022

Politeknik Dergisi

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The performance of the bat algorithm has been improved by updating the velocity equation.  The proposed weight-based bat algorithm (WBBA) hybrited with the k-mean (KM) clustering algorithm to enhance the performance of the clusting analyze.  The proposed WBBA-KM clustering method has been applied on commonly used UCI datasets and has been compared with twelve clustering method in the literature.  The proposed WBBA-KM clustering method has been showed very promising results and can be used to cluster real world problems.

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Section: Outputmentioning

confidence: 99%

WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis

Ibrahim

2022

Politeknik Dergisi

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“…We apply this method to group items in each module based on the categorical responses and continuous response times in which. The number of clusters, M, is determined by the Silhouette index (Rousseeuw, 1987), which is commonly used in clustering analysis (e.g., Rendón et al, 2011;Hämäläinen et al, 2017). This index measures the similarity of an item to its cluster compared to other clusters and its value ranges from −1 to 1.…”

Section: Clustering Analysis On Itemsmentioning

confidence: 99%

The Development of a Multidimensional Diagnostic Assessment With Learning Tools to Improve 3-D Mental Rotation Skills

Wang

et al. 2020

Front. Psychol.

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This study reported on development and evaluation of a learning program that integrated a multidimensional diagnostic assessment with two different learning interventions with the aim to diagnose and improve three-dimensional mental rotation skills. The multidimensional assessment was built upon the Diagnostic Classification Model (DCM) framework that can report the binary mastery on each specific rotation skill. The two learning interventions were designed to train students to use a holistic rotation strategy and a combined analytic and holistic strategy, respectively. The program was evaluated through an experiment paired with multiple exploratory and confirmatory statistical analysis. Particularly, the recently proposed joint models for response times and response accuracy within dynamic DCM framework is applied to assess the effectiveness of the learning interventions. Compared with the traditional assessment on spatial skills, where the tests are timed and number correct is reported as a measure for test-takers' performances, the developed dynamic diagnostic assessment can provide an informative estimate of the learning trajectory for each participant in terms of the strengths and weaknesses in four fine-grained spatial rotation skills over time. Compared with an earlier study that provided initial evidence of the effectiveness of building a multidimensional diagnostic assessment with training tools, the present study improved the assessment and learning intervention design. Using both response times and response accuracy, thus current study additionally evaluated the newly developed program by investigating the effectiveness of two interventions across gender, country and rotation strategy.

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“…Hämäläinen, Jauhiainen, and Kärkkäinen (), finally, recently evaluated seven internal criteria for “prototype‐based” (or centroid‐based) clustering methods, that is, k‐Means and its relatives. Notably, neither Silhouette nor S_Dbw are among them.…”

Section: Configuration/parameterizationmentioning

confidence: 99%

Method evaluation, parameterization, and result validation in unsupervised data mining: A critical survey

Zimmermann

2019

WIREs Data Min & Knowl

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Machine Learning (ML) and Data Mining (DM) build tools intended to help users solve data‐related problems that are infeasible for “unaugmented” humans. Tools need manuals, however, and in the case of ML/DM methods, this means guidance with respect to which technique to choose, how to parameterize it, and how to interpret derived results to arrive at knowledge about the phenomena underlying the data. While such information is available in the literature, it has not yet been collected in one place. We survey three types of work for clustering and pattern mining: (1) comparisons of existing techniques, (2) evaluations of different parameterization options and studies providing guidance for setting parameter values, and (3) work comparing mining results with the ground truth. We find that although interesting results exist, as a whole the body of work on these questions is too limited. In addition, we survey recent studies in the field of community detection, as a contrasting example. We argue that an objective obstacle for performing needed studies is a lack of data and survey the state of available data, pointing out certain limitations. As a solution, we propose to augment existing data by artificially generated data, review the state‐of‐the‐art in data generation in unsupervised mining, and identify shortcomings. In more general terms, we call for the development of a true “Data Science” that—based on work in other domains, results in ML, and existing tools—develops needed data generators and builds up the knowledge needed to effectively employ unsupervised mining techniques. This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Ensemble Methods > Structure Discovery Internet > Society and Culture Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering

Cited by 85 publications

References 40 publications

WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis

WBBA-KM: A Hybrid Weight-Based Bat Algorithm with K-Means Algorithm For Cluster Analysis

The Development of a Multidimensional Diagnostic Assessment With Learning Tools to Improve 3-D Mental Rotation Skills

Method evaluation, parameterization, and result validation in unsupervised data mining: A critical survey

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