by Li, F and Klette, R
Abstract:
This article provides a simple and general way for defining the recovery rate of clustering algorithms using a given family of old clusters for evaluating the performance of the algorithm when calculating a family of new clusters. Under the assumption of dealing with simulated data (i.e., known old clusters), the recovery rate is calculated using one proposed exact (but slow) algorithm, or one proposed approximate algorithm (with feasible run time). © 2009 Springer Berlin Heidelberg.
Reference:
Recovery rate of clustering algorithms (Li, F and Klette, R), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 5414 LNCS, 2009.
Bibtex Entry:
@inproceedings{li2009recoveryalgorithms,
author = "Li, F and Klette, R",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "1058--1069",
title = "Recovery rate of clustering algorithms",
volume = "5414 LNCS",
year = "2009",
abstract = "This article provides a simple and general way for defining the recovery rate of clustering algorithms using a given family of old clusters for evaluating the performance of the algorithm when calculating a family of new clusters. Under the assumption of dealing with simulated data (i.e., known old clusters), the recovery rate is calculated using one proposed exact (but slow) algorithm, or one proposed approximate algorithm (with feasible run time). © 2009 Springer Berlin Heidelberg.",
doi = "10.1007/978-3-540-92957-4_92",
issn = "0302-9743",
eissn = "1611-3349",
language = "eng",
}