An Efficient Approach for Computing Silhouette Coefficients

Zoubi, Moh’d Belal Al; Al-Rawi, Mohammed

doi:10.3844/jcssp.2008.252.255

Cited by 51 publications

(26 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We used k-means clustering to best group the 60 images into 2, 3, 4, 5, and 6 clusters. The fit of these different solutions was quantified using both silhouette coefficients (Al-Zoubi & Rawi, 2008) and the Dunn index (Dunn, 1974). Both measures attempt to identify dense and wellseparated clusters, reflected in higher values.…”

Section: Clustering Of Familiar and Unfamiliar Identitiesmentioning

confidence: 99%

Robust social categorization emerges from learning the identities of very few faces.

Kramer¹,

Young²,

Day³

et al. 2017

Psychological Review

View full text Add to dashboard Cite

Viewers are highly accurate at recognizing sex and race from faces -though it remains unclear how this is achieved. Recognition of familiar faces is also highly accurate across a very large range of viewing conditions, despite the difficulty of the problem. Here we show that computation of sex and race can emerge incidentally from a system designed to compute identity.We emphasise the role of multiple encounters with a small number of people, which we take to underlie human face learning. We use highly variable everyday 'ambient' images of a few people to train a Linear Discriminant Analysis (LDA) model on identity. The resulting model has human-like properties, including a facility to cohere previously unseen ambient images of familiar (trained) people -an ability which breaks down for the faces of unknown (untrained) people. The first dimension created by the identity-trained LDA classifies both familiar and unfamiliar faces by sex, and the second dimension classifies faces by race -even though neither of these categories was explicitly coded at learning. By varying the numbers and types of face identities on which a further series of LDA models were trained, we show that this incidental learning of sex and race reflects covariation between these social categories and face identity, and that a remarkably small number of identities need be learnt before such incidental dimensions emerge. The task of learning to recognise familiar faces is sufficient to create certain salient social categories.

show abstract

Section: Clustering Of Familiar and Unfamiliar Identitiesmentioning

confidence: 99%

Robust social categorization emerges from learning the identities of very few faces.

Kramer¹,

Young²,

Day³

et al. 2017

Psychological Review

View full text Add to dashboard Cite

show abstract

“…Similarly k x and k y can be determined as cor [5] cor [10] cor [15] cor [20] cor [16] cor [21] cor [11] cor [6] cor [1] cor [2] cor [7] cor [17] cor [22] cor [12] cor [3] cor [18] cor [23] cor [8] cor [13] cor [4] cor [19] cor [24] cor [9] cor [14] x…”

Section: ) Building Corner Objectsunclassified

“…For instance, if L = 2, the number of corners is 25 and these are (cor[0], cor [1], ... , cor [24]). …”

Section: ) Building Corner Objectsmentioning

confidence: 99%

A quadtree-based allocation method for a class of large discrete Euclidean location problems

Salhi

Irawan

2015

Computers & Operations Research

View full text Add to dashboard Cite

“…where a(i) is the average distance between points i and all points in cluster A and b (i) is the average distance between the point i and the points in the cluster closest to cluster A, namely cluster B [9]. Interpreting results of silhouette coefficient can be shown on the chart interpretations of the interval coefficient [10], which is in table 1.…”

Section: B Research Methodsmentioning

confidence: 99%

Identification of Tuberculosis Patient Characteristics Using K-Means Clustering

Sari

2016

SJI

View full text Add to dashboard Cite

In Indonesia, tuberculosis remains one of the major health problems unresolved. Indonesia is second ranked in the world as the country with the most tuberculosis cases. The purpose of this research is to study how K-means clustering applied to the treatment of tuberculosis patients data in order to identify the characteristics of tuberculosis patients. The results of K-means clustering validated by gene shaving and silhoutte coefficient. The experiment results indicate the optimum clusters value obtained from the K-mean clustering that has been validated by gene shaving and silhouette coefficient. K-means clustering divided four groups of tuberculosis patients based on their characteristics. There were divided at a category of disease (pulmonary TB, Extra Pulmonary TB and both), the age of the patient and the results of treatment of tuberculosis.

show abstract

An Efficient Approach for Computing Silhouette Coefficients

Cited by 51 publications

References 10 publications

Robust social categorization emerges from learning the identities of very few faces.

Robust social categorization emerges from learning the identities of very few faces.

A quadtree-based allocation method for a class of large discrete Euclidean location problems

Identification of Tuberculosis Patient Characteristics Using K-Means Clustering

Contact Info

Product

Resources

About