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
An integrated robust semi-supervised framework for improving cluster reliability using ensemble method for heterogeneous datasets
TL;DR: An integrated framework, which ensures the reliability of the class labels assigned to a dataset whose class labels are unknown, is proposed, which uses PSO-k-means, k-medoids, c-mean and Expectation Maximization for data clustering.
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Highly-Economized Multi-View Binary Compression for Scalable Image Clustering
TL;DR: Wang et al. as discussed by the authors proposed the Highly-Economized Scalable Image Clustering (HSIC) method, which unifies the binary representation learning and efficient binary cluster structure learning into a joint framework.
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An Effective Two-Stage Clustering Method for Mixing Matrix Estimation in Instantaneous Underdetermined Blind Source Separation and Its Application in Fault Diagnosis
TL;DR: In this article, an effective two-stage clustering algorithm is proposed to estimate the mixing matrix through a combination of hierarchical clustering and K-means, where the sum of frequency points energy in the time-frequency domain is calculated to estimate a number of source signals before clustering, and the initial clustering centers are obtained with a hierarchical algorithm.
Journal ArticleDOI
A clustering-based approach to land valuation in land consolidation projects
TL;DR: In this article, a new land valuation model was developed with the help of clustering algorithms (K-means, K-medoids, Fuzzy C-Means) and Weighted Differential Evolution, a heuristic optimization algorithm, using the most basic nine different parameters affecting the land value.
Journal ArticleDOI
A Crop Canopy Localization Method Based on Ultrasonic Ranging and Iterative Self-Organizing Data Analysis Technique Algorithm
Fang Li,Xiaohu Bai,Yongkui Li +2 more
TL;DR: The fuzzy iterative self-organizing data analysis technique algorithm is adopted to identify the canopy location based on structural characteristics of the crop canopy and provides a practical resolution to localize the canopy for adjusting the height of sprayer boom.
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.