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
Learning best views of 3D shapes from sketch contour
TL;DR: This paper introduces a novel learning-based approach to automatically select the best views of 3D shapes using a new prior, and reveals the connection between sketches and viewpoints by taking context information of their contours into account.
BookDOI
Sentiment Analysis in the Bio-Medical Domain
TL;DR: This introductory chapter reviews the general area of sentiment analysis research and posits a case for incorporating commonsense knowledge in machines, as a means to better understand natural language.
Journal ArticleDOI
PLS regression-based chemometric modeling of odorant properties of diverse chemical constituents of black tea and coffee
Probir Kumar Ojha,Kunal Roy +1 more
TL;DR: In this article, the authors investigated the key structural features which regulate the odorant properties of constituents present in black tea and coffee using regression-based chemometric models and also investigated the structural properties which create the odor difference between tea and black coffee.
Posted Content
A fast and recursive algorithm for clustering large datasets with $k$-medians
TL;DR: In this paper, a recursive stochastic gradient algorithm designed for the $k$-medians loss criterion is proposed, which is very fast and is well adapted to deal with large samples of data that are allowed to arrive sequentially.
Proceedings ArticleDOI
Beyond Value Perturbation: Local Differential Privacy in the Temporal Setting
TL;DR: Li et al. as discussed by the authors proposed local differential privacy in the temporal setting (TLDP) as the privacy notion for time series data, and quantified the utility of a temporal perturbation mechanism in terms of the costs of a missing, repeated, empty, or delayed value.
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.