Journal ArticleDOI
A simple and fast algorithm for K-medoids clustering
Hae-Sang Park,Chi-Hyuck Jun +1 more
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TLDR
Experimental results show that the proposed algorithm takes a significantly reduced time in computation with comparable performance against the partitioning around medoids.Abstract:
This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting initial medoids. The proposed algorithm calculates the distance matrix once and uses it for finding new medoids at every iterative step. To evaluate the proposed algorithm, we use some real and artificial data sets and compare with the results of other algorithms in terms of the adjusted Rand index. Experimental results show that the proposed algorithm takes a significantly reduced time in computation with comparable performance against the partitioning around medoids.read more
Citations
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Journal ArticleDOI
On the Persistence of Clustering Solutions and True Number of Clusters in a Dataset
TL;DR: A notion of persistence of clustering solutions that enables comparing solutions with different number of clusters is quantified; the persistence relates to the range of data-resolution scales over which a clustering solution persists; it is quantification in terms of the maximum over two-norms of all the associated cluster-covariance matrices.
Journal ArticleDOI
Feasibility of an MRI-only workflow for postimplant dosimetry of low-dose-rate prostate brachytherapy: Transition from phantoms to patients.
Reyhaneh Nosrati,Reyhaneh Nosrati,William Y. Song,M. Wronski,Ana Pejović-Milić,Gerard Morton,Greg J. Stanisz +6 more
TL;DR: The proposed susceptibility-based algorithm generated consistent positive contrast for the seeds in phantoms and patients and has great potential to replace the current CT-based practices.
Journal ArticleDOI
Self-Adjusting Variable Neighborhood Search Algorithm for Near-Optimal k-Means Clustering
TL;DR: This article investigates the influence of the most important parameter of randomized neighborhoods formed by the application of greedy agglomerative procedures on the computational efficiency of VNS algorithms and proposes a new VNS-based algorithm (solver), implemented on the graphics processing unit (GPU), which adjusts this parameter.
Journal ArticleDOI
Advanced Electroencephalogram Processing: Automatic Clustering of EEG Components
Diana Rashidovna Golomolzina,Maxim Alexandrovich Gorodnichev,E. A. Levin,Alexander N. Savostyanov,Ekaterina Pavlovna Yablokova,Arthur C. Tsai,Mikhail S. Zaleshin,Anna V. Budakova,Alexander E. Saprygin,Mikhail Anatolyevich Remnev,Nikolay Vladimirovich Smirnov +10 more
TL;DR: A new method and algorithm for automatic clustering of physiologically similar but statistically independent EEG components is described and compared with algorithms implemented in the EEGLab toolbox.
Book ChapterDOI
Rearrangement Scenarios Guided by Chromatin Structure
TL;DR: This paper makes an initial effort towards computing scenarios that respect chromosome conformation, by using Hi-C data to guide the computations of Minimum Local Parsimonious Scenario, and shows that the quality of a clustering of the adjacencies based on Hi- C data is directly correlated to thequality of a rearrangement scenario that is computed.
References
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Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Journal ArticleDOI
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
TL;DR: A new graphical display is proposed for partitioning techniques, where each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation, and provides an evaluation of clustering validity.
Book
Finding Groups in Data: An Introduction to Cluster Analysis
TL;DR: An electrical signal transmission system, applicable to the transmission of signals from trackside hot box detector equipment for railroad locomotives and rolling stock, wherein a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count.
BookDOI
Finding Groups in Data
TL;DR: In this article, an electrical signal transmission system for railway locomotives and rolling stock is proposed, where a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count, and a spike pulse of greater selected amplitude is transmitted, occurring immediately after the axle count pulse to which it relates, whenever an overheated axle box is detected.