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Amorim, M. J. & Cardoso, M. G. M. S. (2015). Clustering Stability and Ground Truth: Numerical Experiments. In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. (pp. 259-264).

M. J. Amorim and M. M. Cardoso,  "Clustering Stability and Ground Truth: Numerical Experiments", in Proc. of the 7th Int. Joint Conf. on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2015, pp. 259-264

@inproceedings{amorim2015_1734873397967,
	author = "Amorim, M. J. and Cardoso, M. G. M. S.",
	title = "Clustering Stability and Ground Truth: Numerical Experiments",
	booktitle = "Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
	year = "2015",
	editor = "",
	volume = "",
	pages = "259-264",
	publisher = "",
	address = "",
	organization = "",
	url = ""
}

TY  - CPAPER
TI  - Clustering Stability and Ground Truth: Numerical Experiments
T2  - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
AU  - Amorim, M. J.
AU  - Cardoso, M. G. M. S.
PY  - 2015
SP  - 259-264
AB  - Stability has been considered an important property for evaluating clustering solutions. Nevertheless, there
are no conclusive studies on the relationship between this property and the capacity to recover clusters
inherent to data (“ground truth”). This study focuses on this relationship resorting to synthetic data
generated under diverse scenarios (controlling relevant factors). Stability is evaluated using a weighted
cross-validation procedure. Indices of agreement (corrected for agreement by chance) are used both to
assess stability and external validation. The results obtained reveal a new perspective so far not mentioned
in the literature. Despite the clear relationship between stability and external validity when a broad range of
scenarios is considered, within-scenarios conclusions deserve our special attention: faced with a specific
clustering problem (as we do in practice), there is no significant relationship between stability and the
ability to recover data clusters.
ER  -