On The General Distance Measure

Jajuga, Krzysztof; Walesiak, Marek; Bąk, Andrzej

doi:10.1007/978-3-642-55721-7_12

Cited by 38 publications

(21 citation statements)

References 3 publications

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confidence: 99%

Finding Groups in Ordinal Data: An Examination of Some Clustering Procedures

Walesiak

Dudek²

2010

Studies in Classification, Data Analysis, and Knowledge Organization

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The article evaluates, based on ordinal data simulated with cluster.Gen function of clusterSim package working in R environment, some cluster analysis procedures containing GDM distance for ordinal data (see [4,18,19]), nine clustering methods and eight internal cluster quality indices for determining the number of clusters. Seventy two clustering procedures are evaluated based on simulated data originating from a variety of models. Models contain the known structure of clusters and differ in the number of true dimensions, the number of categories for each variable, the density and shape of clusters, the number of true clusters, the number of noisy variables. Each clustering result was compared with the known cluster structure from models applying Hubert and Arabie's [2] corrected Rand index.

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confidence: 99%

Finding Groups in Ordinal Data: An Examination of Some Clustering Procedures

Walesiak

Dudek²

2010

Studies in Classification, Data Analysis, and Knowledge Organization

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“…The TOPSIS multidimensional linear ordering method [31,32] used in the study allowed a synthetic assessment of the subjective natural attractiveness of farms simultaneously in all six of its dimensions. The synthetic index allowed the creation of four classes of subjective assessment of farm attractiveness-from the highest rated green farms to the lowest rated Black farms.…”

Section: Discussionmentioning

confidence: 99%

“…The construction of the synthetic indicator by this method requires the calculation of the distance of each object (farm) from the adopted best and worst condition. Due to the fact that the variables were measured on an ordinal rather than an interval scale, the most universal generalized distance measure (GDM) was used to determine the distance [31,32].…”

Section: Methodsmentioning

confidence: 99%

A New Approach to Farm Biodiversity Assessment

2019

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A reduction in biodiversity due to farming operations has been broadly reported. As a solution, policy makers in the European Union have introduced several programs in recent years to enhance biodiversity on farms but these have met with only limited success. One of the main reasons for this lack of success is that the experience, knowledge and attitudes of farmers are not taken into account when biodiversity conservation programs are formulated. Farmers’ opinions must be taken into consideration in the creation of future programs, which should also include an assessment by the farmers of the value of the nature of their farms. In this study, farmers were asked to express their opinion on the attractiveness of their farm in relation to pollinators, game, birds, amphibians, reptiles, rodents and non-crop (wild) plants. Data were then analysed using a novel method in which each farm was assigned to one of four categories. High nature-value farms contained more natural features in the landscape, such as individual trees or ponds. Socio-economic factors, such as gender, also influenced the farm assessments; female farmers were more critical compared to their male counterparts. The establishment of field margins or watercourse by the farmers significantly increased the possibility that the farm would be classified as attractive.

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“…6. Run a cluster analysis with one of clustering methods based on a distance appropriate to nonmetric data (GDM2 for ordinal data -see Jajuga et al [2003]; Sokal and Michener distance for nominal data) with the selected m − h variables.…”

Section: Characteristics Of the Hinov Methods And Its Modificationsmentioning

confidence: 99%

Identification of Noisy Variables for Nonmetric and Symbolic Data in Cluster Analysis

Walesiak

Dudek

2008

Data Analysis, Machine Learning and Applications

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A proposal of an extended version of the HINoV method for the identification of the noisy variables (Carmone et al [1999]) for nonmetric, mixed, and symbolic interval data is presented in this paper. Proposed modifications are evaluated on simulated data from a variety of models. The models contain the known structure of clusters. In addition, the models contain a different number of noisy (irrelevant) variables added to obscure the underlying structure to be recovered.

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On The General Distance Measure

Cited by 38 publications

References 3 publications

Finding Groups in Ordinal Data: An Examination of Some Clustering Procedures

Finding Groups in Ordinal Data: An Examination of Some Clustering Procedures

A New Approach to Farm Biodiversity Assessment

Identification of Noisy Variables for Nonmetric and Symbolic Data in Cluster Analysis

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