A class is defined as a particular set of parameter values and their associated model. A classification is defined as a set of classes and the probabilities of each class. AutoClass generates various reports that can give a way of viewing the current classification. We tried to find a suitable form of results presentation for the classes generated on the ticket sales data. Finally we decided to use a kind of decision tree, where nodes are attributes which mainly contribute by splitting of examples into various clusters as depicted in Fig.
Edges define particular values of given attributes which are typical for examples on given path.
Similarly, the price of the tickets in the clusters is expressed by its color, with higher-priced clusters being indicated by darker shadings. The best clustering we got using AutoClass produced 11 clusters. These attributes clearly separate one cluster with tickets sold based on orders, which are usually tickets for large groups and therefore with highest prices, as opposed to tickets sold to individual tourists at the entrance gate. The second most important attribute for clustering was total price of the ticket separating a second cluster with expensive tickets for large groups bought with no pre-ordering. As third attribute, the nationality of visitors further split the data into two groups - tickets sold to foreigners and tickets for Czech visitors with the latter forming the vast majority of all visitors.