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1

Employing Similarity Measures to Examine the Development of Technical Infrastructure in Polish Counties

100%
Nermend K.
Folia Oeconomica Stetinensia
|
2008
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vol. 7
|
issue 1
87-97
EN
The article presents a comparison made with reference to three similarity measures and Minkowski measures, namely urban measure, the Euclidean measure and Chebyshev measure, as well as the variants of the Euclidean measure and square of the Euclidean measure. Their properties were compared and synthetic measures were created on this basis. Infrastructural-technical variables referring to Polish counties were used as data in the creation of measures.
2
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A new similarity measure for rankings obtained in MCDM problems using different normalization techniques

80%
KACPRZAK D.
Operations Research and Decisions
|
2024
|
vol. 34
|
issue 2
47-65
EN
The paper presents an analysis of the impact of normalization techniques on the ranking of alternatives obtained using the combined compromise solution (CoCoSo) method. Similarity measures known from the literature and a new measure called the TOPSIS similarity measure (TOPSIS-SM) are used to assess the resulting rankings. This new measure is based on the TOPSIS algorithm, where the arithmetic mean of the considered rankings is taken as the ideal solution. In contrast, the antiideal solution is divided into a minimum and a maximum solution, which exhibit maximum separation from the ideal solution. The results obtained by this new method are different from those obtained using other similarity measures known from the literature.
3
Content available remote

Kvalita modelů a volba parametrů počítačové textové analýzy v programu COOA

70%
Čepelák V.
Data a výzkum - SDA Info (Data and Research - SDA Info)
|
2013
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vol. 7
|
issue 1
7-27
EN
This article examines the reliability of statistical models that use visualization of word distances using computer-assisted text analysis. This study looks at the choice of parameters in the COOA - software for word co-occurrence analysis. The word co-occurrence analysis enables visualization of text structure through the exploration of the number of co-occurrences of words. The data visualization provided by a multi-dimensional scaling (MDS) procedure is susceptible to a particular form of error. The nonlinear relationship between words with significantly different frequencies lies at the root of this problem where words with higher frequencies are placed in the middle of a two-dimensional MDS map visualization. Words with lower frequency, on the other hand, are forced by the MDS estimator to the edge of the two-dimensional map and their estimated spatial positions are unstable. These two processes are potentially a major source of error in making inferences. One solution for reducing this source of error is to (a) reduce the number of words in a model or (b) increase of the number of model dimensions. This article, however, suggests that a detailed investigation of the word structure and a thorough analysis of the error sources and their meaningful interpretation may be a better solution.
4
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Logarithmic similarity measures on Pythagorean fuzzy sets in admission process

61%
ARORA H. D., NAITHANI A.
Operations Research and Decisions
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2022
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vol. 32
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issue 1
5-24
EN
The intuitionistic fuzzy sets (IFSs) have a more significant contribution to describing and dealing with uncertainty. The intuitionistic fuzzy measure is a significant consideration in the field of IFSs theory. However, Pythagorean fuzzy sets (PFSs) are an extension of the IFSs. PFSs are more capable of modelling uncertainties than IFSs in real-world decision-making scenarios. The majority of PFSs research has concentrated on establishing decision-making frameworks. A similarity measure is a key concept which measures the closeness of PFSs. IFSs-based similarity measures have been proposed in the literature. This type of similarity measure, however, has a drawback since it cannot satisfy the axiomatic definition of similarity by offering counter-intuitive examples. For this study, a similarity-based on logarithmic function for Pythagorean fuzzy sets (PFSs) is proposed as a solution to the problem. A decision-making approach is presented to ascertain the suitability of careers for aspirants. Additionally, numerical illustration is applied to determine the strength and validity of the proposed similarity measures. The application of the proposed similarity measures is also presented in this article. A comparison of the suggested measures with the existing ones is also demonstrated to ensure the reliability of the measures. The results show that the proposed similarity measures are efficient and reasonable from both numerical and realistic assessments.
5
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The effect of binary data transformation in categorical data clustering

61%
Cibulková J., Šulc Z., Sirota S., Rezanková H.
Statistics in Transition new series
|
2019
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vol. 20
|
issue 2
33-47
EN
This paper focuses on hierarchical clustering of categorical data and compares two approaches which can be used for this task. The first one, an extremely common approach, is to perform a binary transformation of the categorical variables into sets of dummy variables and then use the similarity measures suited for binary data. These similarity measures are well examined, and they occur in both commercial and non-commercial software. However, a binary transformation can possibly cause a loss of information in the data or decrease the speed of the computations. The second approach uses similarity measures developed for the categorical data. But these measures are not so well examined as the binary ones and they are not implemented in commercial software. The comparison of these two approaches is performed on generated data sets with categorical variables and the evaluation is done using both the internal and the external evaluation criteria. The purpose of this paper is to show that the binary transformation is not necessary in the process of clustering categorical data since the second approach leads to at least comparably good clustering results as the first approach.
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