Handling Tree-Structured Values in RapidMiner

Attribute value types play an important role in mostly every datamining task. Most learners, for instance, are restricted to particular value types. The usage of such learners is just possible after special forms of preprocessing. RapidMiner most commonly distinguishes between nominal and numerical values which are well-known to every RapidMineruser. Although, covering a great fraction of attribute types being present in nowadays datamining tasks, nominal and numerical attribute values are not sufficient for every type of feature. In this work we are focusing on attribute values containing a tree-structure. We are presenting the handling and especially the possibilities to use tree-structured data for modelling. Additionally, we are introducing particular tasks which are offering tree-structured data and might benefit from using those structures for modelling. All methods presented in this paper are contained in the Information Extraction Plugin for RapidMiner.