TESI DOCTORAL - La Salle

B.2. Clustering indeterminacies in multimodal data sets
clustering count
clustering count
clustering count
200
100
InternetAds Baseline
0
0 0.5 1
φ (NMI)
(a) Baseline
(multimodal)
200
100
InternetAds Baseline M1
0
0 0.5 1
φ (NMI)
(f) Baseline
(object)
200
100
InternetAds Baseline M2
0
0 0.5 1
φ (NMI)
(k) Baseline
(collateral)
clustering count
clustering count
clustering count
200
100
InternetAds PCA
0
0 0.5 1
φ (NMI)
(b) PCA (multimodal)
200
100
InternetAds PCA M1
0
0 0.5 1
φ (NMI)
(g) PCA (object)
200
100
InternetAds PCA M2
0
0 0.5 1
φ (NMI)
(l) PCA (collateral)
clustering count
clustering count
clustering count
200
100
InternetAds ICA
0
0 0.5 1
φ (NMI)
(c) ICA (multimodal)
200
100
InternetAds ICA M1
0
0 0.5 1
φ (NMI)
(h) ICA (object)
200
100
InternetAds ICA M2
0
0 0.5 1
φ (NMI)
(m) ICA (collateral)
clustering count
clustering count
clustering count
200
100
InternetAds NMF
0
0 0.5 1
φ (NMI)
(d) NMF (multimodal)
200
100
InternetAds NMF M1
0
0 0.5 1
φ (NMI)
(i) NMF (object)
200
100
InternetAds NMF M2
0
0 0.5 1
φ (NMI)
(n) NMF (collateral)
clustering count
clustering count
clustering count
200
100
InternetAds RP
0
0 0.5 1
φ (NMI)
(e) RP (multimodal)
200
100
InternetAds RP M1
0
0 0.5 1
φ (NMI)
(j) RP (object)
200
100
InternetAds RP M2
0
0 0.5 1
φ (NMI)
(o) RP (collateral)
Figure B.15: Histograms of the φ (NMI) values on the InternetAds data set obtained on the
following data representations.
B.2.5 Summary
Repeating the formula employed in section B.1, table B.4 presents the φ (NMI) values attained
by the top clustering solution achieved by the best representative of each one of the five
families of clustering algorithms employed in this work (i.e. agglo, bagglo, direct, graph,
rb and rbr), referring the employed type of representation (baseline, PCA, ICA, NMF or
RP) and modality (multimodal –MM–, mode #1 –M1– or mode #2 –M2–). The idea
is to present a condensed view of the influence of the data representation and clustering
algorithm selection indeterminacies.
Notice the distinct ordering of the five families of clustering algorithms in every data
set. A clear indicator of the algorithm selection indeterminacy, is the fact that the rbr type
of algorithms yield the top clustering solution in three of the four data sets, while offering
the poorest performance in the InternetAds collection.
The indeterminacy regarding the use of multimodal or unimodal data representations
also becomes evident, as in two of the data sets (Corel and IsoLetters) the multimodal
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