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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Proceedings ArticleDOI
BL-ECD: Broad Learning based Enterprise Community Detection via Hierarchical Structure Fusion
TL;DR: Wang et al. as discussed by the authors proposed a broad learning based community detection framework named "HeterogeneoUs Multi-sOurce ClusteRing" (HUMOR), which detects a set of micro community structures of the employees based on each of the socialization activities respectively.
Journal ArticleDOI
Deblender: a semi-/unsupervised multi-operational computational method for complete deconvolution of expression data from heterogeneous samples.
Konstantina Dimitrakopoulou,Elisabeth Wik,Elisabeth Wik,Lars A. Akslen,Lars A. Akslen,Inge Jonassen +5 more
TL;DR: Deblender is presented, a flexible complete deconvolution tool operating in semi−/unsupervised mode based on the user’s access to known marker gene lists and information about cell/tissue composition that corroborate that Deblender can be a valuable tool to improve understanding of gene expression datasets with implications for prediction and clinical utilization.
Journal ArticleDOI
An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach
TL;DR: This paper introduces Hk-medoids, a modified version of the standard k-medoid algorithm that extends the algorithm for the problem of clustering complex heterogeneous objects that are described by a diversity of data types, e.g. text, images, structured data and time series.
Journal ArticleDOI
Clustering and Characterization of the Lactation Curves of Dairy Cows Using K-Medoids Clustering Algorithm
TL;DR: The presented model can be used as a tool to refine characterization on the typical LC patterns, excluding atypical patterns as exceptional cases.
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.