TESI DOCTORAL - La Salle

Contents
2.2.6 Consensus functions based on reinforcement learning . . . . . . . . . 40
2.2.7 Consensus functions based on interpeting object similarity as data . 40
2.2.8 Consensus functions based on cluster centroids . . . . . . . . . . . . 41
2.2.9 Consensus functions based on correlation clustering . . . . . . . . . 41
2.2.10 Consensus functions based on search techniques . . . . . . . . . . . . 42
2.2.11 Consensus functions based on cluster ensemble component selection 42
2.2.12 Other interesting works on consensus clustering . . . . . . . . . . . . 43
3 Hierarchical consensus architectures 45
3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.2 Random hierarchical consensus architectures . . . . . . . . . . . . . . . . . 49
3.2.1 Rationale and definition . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.2.2 Computational complexity . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2.3 Running time minimization . . . . . . . . . . . . . . . . . . . . . . . 54
3.2.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.3 Deterministic hierarchical consensus architectures . . . . . . . . . . . . . . . 69
3.3.1 Rationale and definition . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.3.2 Computational complexity . . . . . . . . . . . . . . . . . . . . . . . . 71
3.3.3 Running time minimization . . . . . . . . . . . . . . . . . . . . . . . 72
3.3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
3.4 Flat vs. hierarchical consensus . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.4.1 Running time comparison . . . . . . . . . . . . . . . . . . . . . . . . 88
3.4.2 Consensus quality comparison . . . . . . . . . . . . . . . . . . . . . . 96
3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
3.6 Related publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4 Self-refining consensus architectures 109
4.1 Description of the consensus self-refining procedure . . . . . . . . . . . . . . 110
4.2 Flat vs. hierarchical self-refining . . . . . . . . . . . . . . . . . . . . . . . . 112
4.2.1 Evaluation of the consensus-based self-refining process . . . . . . . . 116
4.2.2 Evaluation of the supraconsensus process . . . . . . . . . . . . . . . 120
4.3 Selection-based self-refining . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
4.3.1 Evaluation of the selection-based self-refining process . . . . . . . . . 124
4.3.2 Evaluation of the supraconsensus process . . . . . . . . . . . . . . . 126
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
4.5 Related publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
xii