Abstract
Redescription mining is a field of knowledge discovery that aims to find different descriptions of subsets of elements in the data by using two or more disjoint sets of descriptive attributes. The ability to find connections between different sets of descriptive attributes and provide a more comprehensive set of rules makes it very useful in practice. In this work, we introduce redescription mining algorithm for generating and iteratively improving a redescription set of user defined size based on multi-target Predictive Clustering Trees. This approach uses information about element membership in different generated rules to search for new redescriptions and is able to produce highly accurate, statistically significant redescriptions described by Boolean, nominal or numeric attributes. As opposed to current tree-based approaches that use multi-class or binary classification, we explore benefits of using multi target classification and regression to create redescriptions. The process of iterative redescription set improvement is illustrated on the dataset describing 199 world countries and their trading patterns. The performance of the algorithm is compared against the state of the art redescription mining algorithms.
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Acknowledgement
The authors would like to acknowledge the European Commission’s support through the MAESTRA project (Gr. no. 612944), the MULTIPLEX project (Gr.no. 317532), the InnoMol project (Gr. no. 316289), and support of the Croatian Science Foundation (Pr. no. 9623: Machine Learning Algorithms for Insightful Analysis of Complex Data Structures).
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A Appendix
A Appendix
We present several shorter redescriptions mined by the CLUS-RM and the ReReMi algorithm. The full names of the attributes used in redescription queries can be seen in Fig. 5.
In Table 3 we show two very accurate redescriptions mined with the ReReMi algorithm and compare it to two redescriptions mined with the CLUS-RM.
In Table 4, we present two redescriptions containing conjunction and disjunction operator obtained with the ReReMi algorithm, and two redescriptions containing conjunctions and negations obtained with the CLUS-RM algorithm. This examples demonstrate the main difference between the methodologies. The ReReMi algorithm uses disjunction operator often in redescription construction whereas the CLUS-RM mostly uses conjunction operator to construct redescriptions.
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Mihelčić, M., Džeroski, S., Lavrač, N., Šmuc, T. (2016). Redescription Mining with Multi-target Predictive Clustering Trees. In: Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2015. Lecture Notes in Computer Science(), vol 9607. Springer, Cham. https://doi.org/10.1007/978-3-319-39315-5_9
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