The Choice of Metrics for Clustering Algorithms

Grabusts, Peter

doi:10.17770/etr2011vol2.973

Cited by 25 publications

(15 citation statements)

References 5 publications

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“…The principal components analysis method was also excluded as this technique requires the variables to be continuous. Instead, we used the Manhattan distance/city-block metric (Grabusts 2011) to evaluate the distance between the various points (Fab-spaces). This metric was appropriate as it identifies the distance between two points as the sum of the absolute differences of all their Cartesian coordinates (i.e.…”

Section: Clustering Methodologymentioning

confidence: 99%

Through entrepreneurs’ eyes: the Fab-spaces constellation

Mortara

Parisot²

2016

International Journal of Production Research

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Fabrication spaces (Fab-spaces) provide individuals with access to numerous manufacturing equipment (including additive manufacturing), to carry out different types of projects. Although scholars are starting to speculate about the importance of these new organisational forms and their potential for future distributed innovation and production ecologies, this phenomenon is still largely unexplored. Building on existing multidisciplinary research, this paper offers the first empirical analysis of existing fab-spaces as providers of knowledge and production competencies. Amongst all the possible perspectives to derive a framework, we choose that of fab-space users who have an entrepreneurial intention. After deriving an analytical framework to position fab-spaces in the current academic discourse, the paper develops a classification, which considers the competences available to entrepreneurs, via fab-spaces, in conjunction with how these competences are provided. The resulting map reveals the complementarities amongst the different fab-spaces. It also shows that the current portfolio of fab-spaces supports mainly the distribution of innovation across locations and social groups. Several types of fab-spaces are currently well placed to support the transition from innovation to manufacturing, but their geographical distribution and range of manufacturing capabilities are not yet enough to provide a fully distributed manufacturing model. This study has practical consequences for entrepreneurs, in the better identification of the appropriate fab-spaces for their needs, and for policy-makers, to help position the different types of fab-spaces as elements for national systems of innovation and production.

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Section: Clustering Methodologymentioning

confidence: 99%

Through entrepreneurs’ eyes: the Fab-spaces constellation

Mortara

Parisot²

2016

International Journal of Production Research

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“…1.2. CD: CD is also known as maximum value distance, 45 Lagrange, 22 and chessboard distance. 46 This distance is appropriate in cases when two objects are to be defined as different if they are different in any one dimension.…”

Section: Distance Measures Reviewmentioning

confidence: 99%

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

et al. 2019

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The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested examples and the training examples. This raises a major question about which distance measures to be used for the KNN classifier among a large number of distance and similarity measures available? This review attempts to answer this question through evaluating the performance (measured by accuracy, precision, and recall) of the KNN using a large number of distance measures, tested on a number of real-world data sets, with and without adding different levels of noise. The experimental results show that the performance of KNN classifier depends significantly on the distance used, and the results showed large gaps between the performances of different distances. We found that a recently proposed nonconvex distance performed the best when applied on most data sets comparing with the other tested distances. In addition, the performance of the KNN with this top performing distance degraded only *20% while the noise level reaches 90%, this is true for most of the distances used as well. This means that the KNN classifier using any of the top 10 distances tolerates noise to a certain degree. Moreover, the results show that some distances are less affected by the added noise comparing with other distances.

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“…where p is a positive value. It is the Manhattan distance when p = 1 , and the Euclidean distance when p = 2 , whereas the Chebyshev distance is a variant of Minkowski distance where p = ∞ . This is also known as maximum value distance, 23 Lagrange, 17 and chessboard distance, 24 and can be formulated as:…”

Section: Methodsmentioning

confidence: 99%

Robust Distance Measures for kNN Classification of Cancer Data

Ehsani

Drabløs

2020

Cancer Inform

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The k-Nearest Neighbor ( kNN) classifier represents a simple and very general approach to classification. Still, the performance of kNN classifiers can often compete with more complex machine-learning algorithms. The core of kNN depends on a “guilt by association” principle where classification is performed by measuring the similarity between a query and a set of training patterns, often computed as distances. The relative performance of kNN classifiers is closely linked to the choice of distance or similarity measure, and it is therefore relevant to investigate the effect of using different distance measures when comparing biomedical data. In this study on classification of cancer data sets, we have used both common and novel distance measures, including the novel distance measures Sobolev and Fisher, and we have evaluated the performance of kNN with these distances on 4 cancer data sets of different type. We find that the performance when using the novel distance measures is comparable to the performance with more well-established measures, in particular for the Sobolev distance. We define a robust ranking of all the distance measures according to overall performance. Several distance measures show robust performance in kNN over several data sets, in particular the Hassanat, Sobolev, and Manhattan measures. Some of the other measures show good performance on selected data sets but seem to be more sensitive to the nature of the classification data. It is therefore important to benchmark distance measures on similar data prior to classification to identify the most suitable measure in each case.

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The Choice of Metrics for Clustering Algorithms

Cited by 25 publications

References 5 publications

Through entrepreneurs’ eyes: the Fab-spaces constellation

Through entrepreneurs’ eyes: the Fab-spaces constellation

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

Robust Distance Measures for kNN Classification of Cancer Data

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