Highly scalable and robust rule learner: performance evaluation and comparison

• Published in: IEEE transactions on cybernetics 2006-02, Vol.36 (1), p.32-53
• Main Authors: Kurgan, L.A., Cios, K.J., Dick, S.
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Bioinformatics; Business; Companies; Complexity; Computer science; Construction; Data mining; Database Management Systems; Databases, Factual; DataSqueezer; Decision support systems; Decision Support Techniques; Decision trees; Drugs; Extraction; Information Storage and Retrieval - methods; Intelligence; machine learning; Missing data; Production; Real time systems; Robustness; rule induction; rule learner; State of the art; Studies
• ISSN: 1083-4419; 2168-2267; 1941-0492; 2168-2275
• DOI: 10.1109/TSMCB.2005.852983

Business intelligence and bioinformatics applications increasingly require the mining of datasets consisting of millions of data points, or crafting real-time enterprise-level decision support systems for large corporations and drug companies. In all cases, there needs to be an underlying data mining system, and this mining system must be highly scalable. To this end, we describe a new rule learner called DataSqueezer. The learner belongs to the family of inductive supervised rule extraction algorithms. DataSqueezer is a simple, greedy, rule builder that generates a set of production rules from labeled input data. In spite of its relative simplicity, DataSqueezer is a very effective learner. The rules generated by the algorithm are compact, comprehensible, and have accuracy comparable to rules generated by other state-of-the-art rule extraction algorithms. The main advantages of DataSqueezer are very high efficiency, and missing data resistance. DataSqueezer exhibits log-linear asymptotic complexity with the number of training examples, and it is faster than other state-of-the-art rule learners. The learner is also robust to large quantities of missing data, as verified by extensive experimental comparison with the other learners. DataSqueezer is thus well suited to modern data mining and business intelligence tasks, which commonly involve huge datasets with a large fraction of missing data.