Multivariate statistical methods: A brief review on their modifications and applications

Lipovetsky, Stan

doi:10.3233/mas-220017

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…In this approach, the algorithm endeavors to reveal concealed patterns and relationships within the dataset by grouping similar data points into distinct clusters (Liu et al, 2022). Furthermore, clustering is frequently used in any discipline that involves multivariate data analysis (Lipovetsky, 2022). Hence, it leads to the popularity of this technique that brings the creation a substantial body of literature that underscores the significance of data grouping.…”

Section: Introductionmentioning

confidence: 99%

A Review on Appropriateness of Partitional Clustering Algorithms in Handling Transactional Data

Lubis,

Ramayana

2023

Int J Res Rev

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Clustering is an unsupervised learning that widely used in vast researches area. The technique also utilized in any disciplines that involves multivariate data analysis. In term of transactional data handling, the partitional clustering is promoted as the one method to explore knowledge from several attributes that are related the business. In this paper, we investigate the use of partitional clustering algorithms including k-means, k-medoids, Fuzzy C Means, CLARA, and CLARANS. The present article delineates the various stages that are integral to accomplishing a review. These stages encompass data collection, data pre-processing, determination of the number of clusters, implementation of algorithms, and evaluation of clustering. The study pointed out that k-medoids as the most potential to be implemented in handling transactional data. The algorithm has achieved commendable scores in two out of three metrics, namely Calinski-Harabasz Index and Silhouette Index but not for the Davies-Bouldin Index. Nevertheless, k-medoids algorithm emerges as a formidable tool in handling the transactional data and facilitating enhanced decision-making. It is our hope that the knowledge acquired from this research will leading to progress in diverse fields where transactional data holds a crucial position. Keywords: Partitional Clustering, Transactional Data, k-means, k-medoids, Fuzzy C Mean, CLARA, CLARANS

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Section: Introductionmentioning

confidence: 99%

A Review on Appropriateness of Partitional Clustering Algorithms in Handling Transactional Data

Lubis,

Ramayana

2023

Int J Res Rev

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Prioritization and decision-making: A brief review of methods

Lipovetsky¹

2023

MAS

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Statistical and decision-making techniques for solving prioritization problems are described. These approaches include the analytic hierarchy process (AHP) of the multi-attribute decision-making and its extension to the statistical modeling and testing, scaling techniques of priority estimation, maximum difference models, identification of key drivers in regression, and other methods. The described techniques have been widely applied and proved to be helpful for identification and ordering the most important items in solving various marketing research and decision-making problems.

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Multivariate statistical methods: A brief review on their modifications and applications

Cited by 2 publications

References 27 publications

A Review on Appropriateness of Partitional Clustering Algorithms in Handling Transactional Data

A Review on Appropriateness of Partitional Clustering Algorithms in Handling Transactional Data

Prioritization and decision-making: A brief review of methods

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