# Learning-Based Dissimilarity for Clustering Categorical Data
## 4 Experimental Framework
### Cluster Analysis and Datasets  
For comparatively evaluating Learning-Based Dissimilarity, we chose cluster analysis, although other data mining tasks, such as anomaly detection, could have been chosen. Since the behavior of a similarity relation affects the output of, but does not depend on, the cluster analysis technique, we picked KMeans++ [15]. The rationale behind this decision is that KMeans++ is, perhaps, the most popular clustering algorithm, for it has been thoroughly studied [18], and has found a number of applications. The key problem with KMeans++ is that it needs to be given \(k\), the target number of problems to be found. However, in our case, this is not a problem, since, as shall be described later, we have turned a classification problem into a clustering one, and so each dataset contains both objects and their intended class, and so is taken as the ground truth.  
We used 55 datasets collected from the UCI Machine Learning Repository [11]. Our datasets were split into two groups. The first group contains 26 datasets, where each object's properties were originally described using only categorical attributes. The second group, however, contains 29 datasets that were artificially made to satisfy the above property; put differently, every object in a dataset of this kind is as it was originally asserted, except that we have removed from it any attribute that is not categorical. The rationale behind this pre-processing decision is to get away from whatever possible negative impact there may be for these kinds of variable in our results. Table 4 lists the datasets used in our investigation, together with key properties thereof.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset Name** & **\#Ft** & **\#Ob** & **Dataset Name** & **\#Ft** & **\#Ob** \\ \hline allbp\_complete & 17 & 3772 & drug\_consumption\_Mushrooms & 6 & 1885 \\ arrhythmia & 74 & 452 & drug\_consumption\_Nicotine & 6 & 1885 \\ australian & 9 & 690 & drug\_consumption\_Semer & 6 & 1885 \\ balloons & 5 & 20 & drug\_consumption\_VSA & 6 & 1885 \\ breast-cancer & 10 & 286 & hayes-roth & 5 & 160 \\ car & 7 & 1728 & hcc & 28 & 165 \\ cervical\_cancer\_biopsy & 21 & 858 & hiv-1 & 9 & 6590 \\ cervical\_cancer\_cytology & 21 & 858 & kr-vs-kp & 37 & 3196 \\ cervical\_cancer\_hinselmann & 21 & 858 & lenses & 5 & 24 \\ cervical\_cancer\_schiller & 21 & 858 & lung-cancer & 57 & 32 \\ chess & 37 & 3196 & lymphography & 19 & 148 \\ crx & 10 & 690 & molecular\_Promoter & 58 & 106 \\ diagnosis1 & 6 & 120 & monks-1 & 7 & 556 \\ diagnosis2 & 6 & 120 & mushroom & 23 & 8125 \\ drug\_consumption\_Alcohol & 6 & 1885 & nursery & 9 & 12,960 \\ drug\_consumption\_Amphet & 6 & 1885 & postoperative-patient-data & 9 & 90 \\ drug\_consumption\_Benzos & 6 & 1885 & primary-tumor & 18 & 339 \\ drug\_consumption\_Ca & 6 & 1885 & shuttle-landing-control & 7 & 16 \\ drug\_consumption\_Cambis & 6 & 1885 & solar-flare1 & 13 & 324 \\ drug\_consumption\_Choc & 6 & 1885 & solar-flare2 & 13 & 1067 \\ drug\_consumption\_Coke & 6 & 1885 & soybean-1 & 36 & 683 \\ drug\_consumption\_Crack & 6 & 1885 & soybean-s & 36 & 47 \\ drug\_consumption\_Ectasy & 6 & 1885 & spect & 23 & 267 \\ drug\_consumption\_Heroin & 6 & 1885 & splice & 61 & 3190 \\ drug\_consumption\_Ketamine & 6 & 1885 & sponge & 45 & 76 \\ drug\_consumption\_LSD & 6 & 1885 & tic-tac-toe & 10 & 958 \\ drug\_consumption\_Legalh & 6 & 1885 & vote & 17 & 435 \\ drug\_consumption\_Meth & 6 & 1885 &  
\end{table}
Table 4: Datasets, as well as some characteristics thereof, used in our experimentation. They were all taken from the UCI Machine Learning Repository [11]. Columns #Ob and #Ft, respectively, stand for number of objects and number of categorical features.