Pleiades: Subspace Clustering and Evaluation
Abstract
Subspace clustering mines the clusters present in locally relevant subsets of the attributes. In the literature, several approaches have been suggested along with different measures for quality assessment. Pleiades provides the means for easy comparison and evaluation of different subspace clustering approaches, along with several quality measures specific for subspace clustering as well as extensibility to further application areas and algorithms. It extends the popular WEKA mining tools, allowing for contrasting results with existing algorithms and data sets.
Cite
Text
Assent et al. "Pleiades: Subspace Clustering and Evaluation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008. doi:10.1007/978-3-540-87481-2_44Markdown
[Assent et al. "Pleiades: Subspace Clustering and Evaluation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2008.](https://mlanthology.org/ecmlpkdd/2008/assent2008ecmlpkdd-pleiades/) doi:10.1007/978-3-540-87481-2_44BibTeX
@inproceedings{assent2008ecmlpkdd-pleiades,
title = {{Pleiades: Subspace Clustering and Evaluation}},
author = {Assent, Ira and Müller, Emmanuel and Krieger, Ralph and Jansen, Timm and Seidl, Thomas},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2008},
pages = {666-671},
doi = {10.1007/978-3-540-87481-2_44},
url = {https://mlanthology.org/ecmlpkdd/2008/assent2008ecmlpkdd-pleiades/}
}