Access the full text.
Sign up today, get DeepDyve free for 14 days.
References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.
5 A Sequential Sampling Framework for Spectral k -Means Based on Efficient Bootstrap Accuracy Estimations: Application to Distributed Clustering DIMITRIOS MAVROEIDIS, Radboud University Nijmegen PANAGIS MAGDALINOS, National and Kapodistrian University of Athens The scalability of learning algorithms has always been a central concern for data mining researchers, and nowadays, with the rapid increase in data storage capacities and availability, its importance has increased. To this end, sampling has been studied by several researchers in an effort to derive sufficiently accurate models using only small data fractions. In this article we focus on spectral k-means, that is, the k-means approximation as derived by the spectral relaxation, and propose a sequential sampling framework that iteratively enlarges the sample size until the k-means results (objective function and cluster structure) become indistinguishable from the asymptotic (infinite-data) output. In the proposed framework we adopt a commonly applied principle in data mining research that considers the use of minimal assumptions concerning the data generating distribution. This restriction imposes several challenges, mainly related to the efficiency of the sequential sampling procedure. These challenges are addressed using elements of matrix perturbation theory and statistics. Moreover, although the main focus is on spectral k-means, we also demonstrate
ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery
Published: Jul 1, 2012
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.