Abstract
In this work, we present the hierarchical object-driven action rules; a hybrid action rule extraction approach that combines key elements from both the classical action rule mining approach, first proposed by Raś and Wieczorkowska (2000), and the more recent object-driven action rule extraction approach proposed by Hajja et al. (2012, 2013), to extract action rules from object-driven information systems. Action rules, as defined in Raś and Wieczorkowska (2000), are actionable tasks that describe possible transitions of instances from one state to another with respect to a distinguished attribute, called the decision attribute. Recently, a new specialized case of action rules, namely object-driven action rules, has been introduced by Hajja et al. (2012, 2013). Object-driven action rules are action rules that are extracted from information systems with temporal and object-based nature. By object-driven information systems, we mean systems that contain multiple observations for each object, in which objects are determined by an attribute that assumingly defines some unique distribution; and by temporally-based information systems, we refer to systems in which each instance is attached to a timestamp that, by definition, must have an intrinsic meaning for each corresponding instance. Though the notion of object-driven and temporal-based action rules had its own successes, some argue that the essence of object-driven assumptions, which is in big part the reason for its effectiveness, are imposing few limitations as well. Object-driven approaches treat entire systems as multi-subsystems for which action rules are extracted from; as a result, more accurate and specific action rules are extracted. However, by doing so, our diverseness of the extracted action rules are much less apparent, compared to the outcome when applying the classical action rule extraction approach, which treats information systems as a whole. For that reason, we propose a hybrid approach which builds a hierarchy of clusters of subsystems; a novel way of clustering through treatments responses similarities is introduced.
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Acknowledgments
The authors would like to thank Dr. Danuta Chojnacka-Wa̧dołowska and Dr. Cecylia Konopka, both from Children’s Memorial Health Institute in Warsaw; and Dr. Ryszard Gubrynowicz from PJIIT, for their help with data collection and providing medical diagnoses.
This project was partially supported by the Research Center of PJIIT, supported by the Polish Ministry of Science and Higher Education. It is also based upon work supported by the National Science Foundation under Grant No. OISE-0730065.
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Hajja, A., Raś, Z.W. & Wieczorkowska, A.A. Hierarchical object-driven action rules. J Intell Inf Syst 42, 207–232 (2014). https://doi.org/10.1007/s10844-013-0291-2
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DOI: https://doi.org/10.1007/s10844-013-0291-2