svcR: An R Package for Support Vector Clustering improved with Geometric Hashing applied to Lexical Pattern Discovery
Figure 9: Class distribution over clusters resulting from HC (left 4 columns), KM (centre 4 columns) and svcR (right 4 columns). Only three first classes are displayed (over 6) i.e. C1 to C3. These classes are hand-made built and each term is tagged in the data matrix with one of these classes. After clustering, we collect class membership of terms for a given cluster. In the table, each line is the distribution of a given cluster (different over each method HC, KM or svcR). Each line shows the weigth in percent for each class in the cluster. The last line represent a baseline, showing how should be the weigth of a class if all terms should form a unic cluster. Hence for instance class 1 represents 12% of the set of terms. On each line we dispaly also (column #) the number of terms belonging to the cluster.
['[BOLD] HC  [BOLD] C1', '[BOLD] HC  [BOLD] C2', '[BOLD] HC  [BOLD] C3', '[BOLD] HC  [BOLD] #', '[BOLD] KM  [BOLD] C1', '[BOLD] KM  [BOLD] C2', '[BOLD] KM  [BOLD] C3', '[BOLD] KM  [BOLD] #', '[BOLD] svcR  [BOLD] C1', '[BOLD] svcR  [BOLD] C2', '[BOLD] svcR  [BOLD] C3', '[BOLD] svcR  [BOLD] #']
As Data contains 6 classes and svcR approaches with JRB kernel induces extraction of 17 clusters we settle 30 clusters extraction as settings for both KM and HC function. The right column of each result set means the size of each cluster. The last line means distribution over classes from the whole dataset as baseline (it means that 12% of terms belong to class 1, 19% to class 2, 20% to class 3, 20% to class 4, 16% to class 4, 12% to class 6 and size of set is 1893 terms). First we can observe that distribution profile in cluster size is similar between HC and svcR. Secondly, looking at over-representation of classes over clusters HC and KM do not achieve better discrimination of terms across the 6 classes some clusters are better over. Language ambiguities seem to be a real bottleneck for all methods when usage is based on a Jaccard-Radial Kernel. But what happens when string kernels are used ?