Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 331-357 |
| Journal | Machine Learning |
| Volume | 98 |
| Issue number | 1-2 |
| DOIs | |
| Publication status | Published - 2013 |
| MoE publication type | A1 Journal article-refereed |
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Keywords
- Bregman divergence
- consensus clustering
- ensemble clustering
- evidence accumulation
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Probabilistic consensus clustering using evidence accumulation. / Lourenço, André; Rota Bulò, Samuel (Corresponding Author); Rebagliati, Nicola; Fred, Ana L.N.; Figueiredo, Mário A.T.; Pelillo, Marcello.
In: Machine Learning, Vol. 98, No. 1-2, 2013, p. 331-357.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Probabilistic consensus clustering using evidence accumulation
AU - Lourenço, André
AU - Rota Bulò, Samuel
AU - Rebagliati, Nicola
AU - Fred, Ana L.N.
AU - Figueiredo, Mário A.T.
AU - Pelillo, Marcello
PY - 2013
Y1 - 2013
N2 - Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into a pairwise co-association matrix, thus avoiding the label correspondence problem, which is intrinsic to other clustering ensemble schemes. In this paper, we propose a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix. Our solution determines probabilistic assignments of data points to clusters by minimizing a Bregman divergence between the observed co-association frequencies and the corresponding co-occurrence probabilities expressed as functions of the unknown assignments. We additionally propose an optimization algorithm to find a solution under any double-convex Bregman divergence. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.
AB - Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into a pairwise co-association matrix, thus avoiding the label correspondence problem, which is intrinsic to other clustering ensemble schemes. In this paper, we propose a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix. Our solution determines probabilistic assignments of data points to clusters by minimizing a Bregman divergence between the observed co-association frequencies and the corresponding co-occurrence probabilities expressed as functions of the unknown assignments. We additionally propose an optimization algorithm to find a solution under any double-convex Bregman divergence. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.
KW - Bregman divergence
KW - consensus clustering
KW - ensemble clustering
KW - evidence accumulation
U2 - 10.1007/s10994-013-5339-6
DO - 10.1007/s10994-013-5339-6
M3 - Article
VL - 98
SP - 331
EP - 357
JO - Machine Learning
JF - Machine Learning
SN - 0885-6125
IS - 1-2
ER -