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Beyond Kmedoids: Sparse Model Based Medoids Algorithm for Representative Selection
[chapter]
2013
Lecture Notes in Computer Science
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In this paper we propose a sparse model based medoids algorithm (Smedoids) which aims to learn a special dictionary. ...
The Kmedoids algorithm is a commonly used unsupervised method, which selects center points as representatives. ...
There are t When adding a new rep
sufficient to describe the da
can be achieved simply by c
ond Kmedoids: Sparse Model Based Medoids Algorithm
ata point j
x ,where k
j
d
x
= and
ref
j ...
doi:10.1007/978-3-642-35728-2_23
fatcat:u3yvteyjnzehhcgdn5xgcw6cpm
Data clustering: 50 years beyond K-means
2010
Pattern Recognition Letters
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Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. ...
useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering. ...
Acknowledgements I would like to acknowledge the National Science Foundation and the Office of Naval research for supporting my research in data clustering, dimensionality reduction, classification, and ...
doi:10.1016/j.patrec.2009.09.011
fatcat:xlertmuqz5fgpn6lrgt5np46nu
High Dimensional Cluster Analysis Using Path Lengths
2018
Journal of Data Analysis and Information Processing
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A Line-of-Sight algorithm is also developed for clustering. A test bank of 12 data sets with varying properties is used to expose the strengths and weaknesses of each technique. ...
A hierarchical scheme for clustering data is presented which applies to spaces with a high number of dimensions ( 3 D N > ). ...
All calculations for this study are performed on the partition data space, P , which represents the integer-based grid of bin locations. ...
doi:10.4236/jdaip.2018.63007
fatcat:cxcpwfihz5ayjdcw5hzcscfxaq
Dissimilarity-based Sparse Subset Selection
[article]
2016
arXiv
pre-print
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modeling and segmentation using representative~models. ...
We show that when the two sets jointly partition into multiple groups, our algorithm finds representatives from all groups and reveals clustering of the sets. ...
-Our proposed algorithm is based on convex programming, hence, unlike algorithms such as Kmedoids, does not depend on initialization. ...
arXiv:1407.6810v2
fatcat:h3ywjodhrfdejbc2zw27cyeuka
Cluster-based multidimensional scaling embedding tool for data visualization
[article]
2022
arXiv
pre-print
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Its algorithm combines the well-known multidimensional scaling (MDS) tool with the k-medoids data clustering technique, and enables hierarchical embedding, sparsification and estimation of 2-dimensional ...
coordinates for additional points. ...
provided by CSC -IT Center for Science and Aalto University's Science-IT Project. ...
arXiv:2209.06614v1
fatcat:ik2bbv5hkveyzh4ewzolspunxy
FedCO: Communication-Efficient Federated Learning via Clustering Optimization
2022
Future Internet
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In order to reduce the communication costs, we first divide the participating workers into groups based on the similarity of their model parameters and then select only one representative, the best performing ...
The updated clustering is used to select new cluster representatives. ...
In addition, we further study our FedCO algorithm for two different scenarios for selecting cluster representatives: a performance threshold-based worker selection versus the single (topperformer) cluster ...
doi:10.3390/fi14120377
fatcat:b5cmlwqsfvflhk3725samyrjn4
A simple approach to sparse clustering
2017
Computational Statistics & Data Analysis
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2016 pre-print arXiv:1602.07277v1 fatcat:c5f7q7ntwvg7vjfdffticblidmOther Versions
Consider the problem of sparse clustering, where it is assumed that only a subset of the features are useful for clustering purposes. ...
In the framework of the COSA method of Friedman and Meulman, subsequently improved in the form of the Sparse K-means method of Witten and Tibshirani, a natural and simpler hill-climbing approach is introduced ...
Another line of research on sparse clustering is based on coordinate-wise testing for mixing. This constitutes the feature selection step. ...
doi:10.1016/j.csda.2016.08.003
fatcat:dfn2wzqqgnbdng5lql4o5necfu
Representative Selection in Nonmetric Datasets
2015
Applied Artificial Intelligence
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In this paper we propose δ-medoids, a novel approach that can be viewed as an extension to the k-medoids algorithm and is specifically suited for sample representative selection from non-metric data. ...
We also show some theoretical bounds on the performance of δ-medoids and the hardness of representative selection in general. ...
We first introduce a simpler, single-iteration δ-representative selection algorithm on which the full δ-medoids algorithm is based. ...
doi:10.1080/08839514.2015.1071092
fatcat:5rog33matvf7bm27qbokdwbx3q
Recovery guarantees for exemplar-based clustering
2015
Information and Computation
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Unfortunately, any model in which edge weights are drawn independently does not include graphs that represent points drawn independently in a metric space. For these graphs, 65 ...
As far as the authors are aware, Theorem 1 provides the first recovery guarantees for k-medoids beyond this regime. 45 Relevant works While the literature on clustering is extensive, three lines of inquiry ...
Acknowledgements We would like to thank the anonymous referees for helpful suggestions. We thank Shi Li and Chris White for helpful suggestions. ...
doi:10.1016/j.ic.2015.09.002
fatcat:tbclzcof6zdvbelmb5zvzxi2ou
Recovery guarantees for exemplar-based clustering
[article]
2014
arXiv
pre-print
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For a certain class of distributions, we prove that the linear programming relaxation of k-medoids clustering---a variant of k-means clustering where means are replaced by exemplars from within the dataset ...
A.N. is especially grateful to Jun Song for his constructive suggestions and for general support during the preparation of this work. ...
We are extremely grateful to Sujay Sanghavi for offering his expertise on clustering and for pointing us in the right directions as we navigated the literature. ...
arXiv:1309.3256v2
fatcat:qyisze4z35eyfmdvxlqwo3dowa
Travel Patterns Analysis Using Tensor-Based Model from Large-Scale License Plate Recognition Data
2022
Journal of Advanced Transportation
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Then, the tensor decomposition and reconstruction algorithms are performed based on extracted feature variables to analyze their influence on travel patterns. ...
As travel patterns are influenced by many variables, a method framework based on the tensor model is proposed to explore the influence of variables on travel characteristics. ...
In this paper, K-means clustering algorithm, Kmedoids clustering algorithm, and maximum-minimum distance clustering algorithm are selected to process the six variables that affect vehicle travel characteristics ...
doi:10.1155/2022/3930795
fatcat:57dbptnzpfd3lpvq6nzn7kbrxu
Mining Music from Large-Scale, Peer-to-Peer Networks
2011
IEEE Multimedia
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For example, even though improved search schemes 2 and recommender systems 3 have been proposed to help users find content, current P2P networks mostly employ simple string-matching algorithms against ...
For this project, we studied the musical content shared by users in Gnutella, 5 then built a song-similarity graph, where the similarity between two songs is based on the number of users that share the ...
Acknowledgments We thank Tomer Tankel for providing the data used in this article, and sharing valuable ideas for its successful analysis. ...
doi:10.1109/mmul.2011.13
fatcat:pmzvyu3sanfb7h2wt5txpaux3a
Variable Clustering via Distributionally Robust Nodewise Regression
[article]
2022
arXiv
pre-print
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an ADMM algorithm for implementation. ...
We study a multi-factor block model for variable clustering and connect it to the regularized subspace clustering by formulating a distributionally robust version of the nodewise regression. ...
Zhou gratefully acknowledges financial support through a start-up grant and the Nie Center for Intelligent Asset Management at Columbia University. ...
arXiv:2212.07944v2
fatcat:g4l7xm3q4zc5zns4goymsz5h7i
Systematic sensor placement for structural anomaly detection in the absence of damaged states
2020
Computer Methods in Applied Mechanics and Engineering
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Reduced order modeling techniques and one-class machine learning algorithms allow to efficiently achieve this goal for a fixed number and location of sensors. ...
In this work we propose to use the variational approximation of sparse Gaussian processes to systematically place a fixed number of sensors over a structure of interest. ...
Acknowledgments This work was partially supported by the Swiss Commission for Technology and Innovation (CTI) under Grant No. 25964.2 PFIW-IW. ...
doi:10.1016/j.cma.2020.113315
fatcat:jrp4pk3vnzghvocks7moy2kkyy
Seasonality modeling of the distribution of Aedes albopictus in China based on climatic and environmental suitability
2019
Infectious Diseases of Poverty
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The aim of the present study was to develop a model based on available observations, climatic and environmental data, and machine learning methods for the prediction of the potential seasonal ranges of ...
The models were assessed based on sensitivity, specificity, and accuracy, using area under curve (AUC). ...
Acknowledgements We thank Di-Zi Yang for her assistance with part of the data collections. ...
doi:10.1186/s40249-019-0612-y
pmid:31791409
pmcid:PMC6889612
fatcat:rho4bmelozfbjn4bqk5jbdjhia
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