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buildClassifier (Contexte) classifyExemple ( Example ex) Instances Instance input Option value : String name: String Description: String has Fig. 7. A partial view of WEKA with the new classification algorithms In WEKA the extension is done through inheritance. The choice of this approach supposes that algorithms are written in Java. The management of heterogeneity through the reuse of algorithms written in other programming languages relies on the competence of the developer. So the next step will consist to provide the mechanism which could easy the adaptation of heterogeneous algorithms. 5.3.2 Managing interoperability in WEKA platform The algorithms implements into the WEKA environment (classifier, cluster and association rule) use as in parameter, the data under an owner format (ARFF, Attribute Relation File Format). They produce at output textual unstructured descriptions. The absence of an explicit description of the characteristics of the models thus built does not allow the reuse of extracted knowledge. From this flat structure could be extracted the main characteristics of each type of model. In the case of the association rules, the model consists of all the rules found by the algorithm. The importance of each rule is evaluated through the metric used by the algorithm. In general it is about confidence. Each rule consists of two sets of items (called Itemset) characterized by their support. The first is being the antecedent or premise and the second the conclusion or consequence. One Item indicates an article or a pair attribute/value of the data source. In the case of sequential motifs, the model is rather made up of a set of sequences (called Sequence). A sequence being a chronological series of Itemsets. Three major concepts permit to characterize the rules independently of the platforms and the algorithms: items, ItemSets and rules. The same reasoning can be applied easily to the other types of model in order to extract their characteristics. Figura 9 presents the process which has been used to export the models from the algorithm of the associate rule family in WEKA.

A Software Architecture for Data Mining Environment