Impact of Base Partitions on Multi-objective and Traditional Ensemble Clustering Algorithms

Jane Piantoni , Katti Faceli (1) , Tieme Sakata (1) , Julio Pereira , Marcilio de Souto (2)

1 Departamento de Computação (DComp)
2 LIFO - Laboratoire d'Informatique Fondamentale d'Orléans

This paper presents a comparative study of cluster ensemble and multi-objective cluster ensemble algorithms. Our aim is to evaluate the extent to which such methods are able to identify the underlying structure hidden in a data set, given different levels of information they receive as input in the set of base partitions (BP). To do so, given a gold/reference partition, we produced nine sets of BP containing properties of interest for our analysis, such as large number of subdivisions of true clusters. We aim at answering questions such as: are the methods able to generate new and more robust partitions than those in the set of BP? are the techniques influenced by poor quality partitions presented in the set of BP?

Mots clés: Clustering; Cluster Ensemble; Multi-objective Clustering; Performance Evaluation

Domaines: Intelligence artificielle [cs.AI]

Jane Piantoni, Katti Faceli, Tieme Sakata, Julio Pereira, Marcilio de Souto. Impact of Base Partitions on Multi-objective and Traditional Ensemble Clustering Algorithms. 22nd International Conference on Neural Information Processing (ICONIP2015), Nov 2015, Istanbul, Turkey. pp.696-704, ⟨10.1007/978-3-319-26532-2_77⟩. ⟨hal-01188966⟩