Journal ArticleDOI
A simple and fast algorithm for K-medoids clustering
Hae-Sang Park,Chi-Hyuck Jun +1 more
Reads0
Chats0
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
More filters
Journal ArticleDOI
A comparative evaluation of novelty detection algorithms for discrete sequences
TL;DR: An experimental comparison of candidate methods for the novelty detection problem applied to discrete sequences is provided to identify which state-of-the-art methods are efficient and appropriate candidates for a given use case.
Posted ContentDOI
Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data
TL;DR: This study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur and compares three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identifies areas that undergo similar evolution during one month.
Journal ArticleDOI
Unsupervised discriminative feature representation via adversarial auto-encoder
TL;DR: This paper attempts to learn a discriminative feature representation for high-dimensional image data via adversarial auto-encoder and takes its advantage into image clustering, which has become a difficult computer vision task recently.
Journal ArticleDOI
Performance analysis of clustering techniques over microarray data: A case study
Rasmita Dash,Bijan Bihari Misra +1 more
TL;DR: This study implements a two stage grading approach over five clustering techniques like hybrid swarm based clustering (HSC), k -means, partitioning around medoids (PAM), vector quantization (VQ) and agglomerative nesting (AGNES) to identify a stable technique.
Journal ArticleDOI
A Novel Outlier Detection Applied to an Adaptive K-Means
TL;DR: A new method of initial centers selection based on data density and a novel approach of outlier detection based onData distance outperformed the traditional K-means because of higher speed and great accuracy acquired.
References
More filters
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.