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
Proceedings ArticleDOI
A K-medoids based clustering scheme with an application to document clustering
TL;DR: The randomized seeding technique is augmented to overcome problem of poor initialization of medoids in PAM algorithm to improve the performance of Pam algorithm on text document clustering.
Journal ArticleDOI
Mixed Integer Linear Programming Optimization of Gas Supply to a Local Market
TL;DR: The sensitivity of the optimal supply chain on the price of local and alternative fuels as well as on the unit price of pipes and storage tanks is studied to illustrate how optimization can be used to shed light on the feasibility of investment in new infrastructure and to support the decision making processes in the energy sector.
Journal ArticleDOI
Adaptive Estimation of Time-Varying Sparse Signals
TL;DR: This work considers the problem of adaptively designing compressive measurement matrices for estimating time-varying sparse signals as a partially observable Markov decision process and adapts two data association heuristics to the compressive sensing paradigm.
Journal ArticleDOI
Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning
TL;DR: In this article, the authors proposed the application of machine learning for signal-noise classification of microseismic data from Pohang, South Korea, while hydraulic stimulation was being conducted.
Book ChapterDOI
Large-Scale Distributed Locality-Sensitive Hashing for General Metric Data
TL;DR: This work proposes Parallel Voronoi LSH, an approach that makes LSH efficient for distributed-memory architectures, and works for very general dissimilarities (in particular, it works for all metric Dissimilarities).
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.