Efficient Monte Carlo clustering in subspaces

Efficient Monte Carlo clustering in subspaces Clustering of high-dimensional data is an important problem in many application areas, including image classification, genetic analysis, and collaborative filtering. However, it is common for clusters to form in different subsets of the dimensions. We present a randomized algorithm for subspace and projected clustering that is both simple and efficient. The complexity of the algorithm is linear in the number of data points and low-order polynomial in the number of dimensions. We present the results of a thorough evaluation of the algorithm using the OpenSubspace framework. Our algorithm outperforms competing subspace and projected clustering algorithms on both synthetic and real-world data sets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Knowledge and Information Systems Springer Journals

Efficient Monte Carlo clustering in subspaces

Loading next page...
 
/lp/springer_journal/efficient-monte-carlo-clustering-in-subspaces-rucixosoGK
Publisher
Springer London
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Computer Science; Information Systems and Communication Service; IT in Business
ISSN
0219-1377
eISSN
0219-3116
D.O.I.
10.1007/s10115-017-1031-7
Publisher site
See Article on Publisher Site

Abstract

Clustering of high-dimensional data is an important problem in many application areas, including image classification, genetic analysis, and collaborative filtering. However, it is common for clusters to form in different subsets of the dimensions. We present a randomized algorithm for subspace and projected clustering that is both simple and efficient. The complexity of the algorithm is linear in the number of data points and low-order polynomial in the number of dimensions. We present the results of a thorough evaluation of the algorithm using the OpenSubspace framework. Our algorithm outperforms competing subspace and projected clustering algorithms on both synthetic and real-world data sets.

Journal

Knowledge and Information SystemsSpringer Journals

Published: Feb 14, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from
Google Scholar,
PubMed
Create lists to
organize your research
Export lists, citations
Read DeepDyve articles
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off