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
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Journal ArticleDOI
An order-based algorithm for minimum dominating set with application in graph mining
David Chalupa,David Chalupa +1 more
TL;DR: A new order-based randomised local search algorithm (RLSo) is proposed to solve minimum dominating set problem in large graphs and indicates that RLSo performs better than both a classical greedy approximation algorithm and two metaheuristic algorithms based on ant colony optimisation and local search.
Journal ArticleDOI
Automatic tree detection from three-dimensional images reconstructed from 360 spherical camera using YOLO v2
Kenta Itakura,Fumiki Hosoi +1 more
TL;DR: A method for tree measurement using 360° spherical cameras, which takes omnidirectional images and an automatic tree detection method from the 3D images was presented, which could automatically estimate some of the structural parameters of trees and contribute to more efficient tree measurement.
Journal ArticleDOI
Clustering big IoT data by metaheuristic optimized mini-batch and parallel partition-based DGC in Hadoop
Rui Tang,Simon Fong +1 more
TL;DR: A new partitioned clustering method that is optimized by metaheuristic is proposed for IoT big data environment that has three main activities: a sample of the dataset is partitioned into mini batches, followed by adjusting the centroids of the mini batches of data.
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Deep segmentation networks predict survival of non-small cell lung cancer
Stephen Baek,Yusen He,Bryan G. Allen,John M. Buatti,Brian J. Smith,Ling Tong,Zhiyu Sun,Jia Wu,Maximilian Diehn,Billy W. Loo,Kristin A. Plichta,Steven N. Seyedin,Maggie Gannon,Katherine R. Cabel,Yusung Kim,Xiaodong Wu +15 more
TL;DR: This retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT) found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals.
Proceedings ArticleDOI
Data clustering using hybrid improved cuckoo search method
TL;DR: A novel metaheuristic method based on k-means and improved cuckoo search to extend the capabilities of traditional clustering methods and results validate that the proposed method outperforms the existing methods.
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.