ABSTRACT
We consider a model in which background knowledge on a given domain of interest is available in terms of a Bayesian network, in addition to a large database. The mining problem is to discover unexpected patterns: our goal is to find the strongest discrepancies between network and database. This problem is intrinsically difficult because it requires inference in a Bayesian network and processing the entire, potentially very large, database. A sampling-based method that we introduce is efficient and yet provably finds the approximately most interesting unexpected patterns. We give a rigorous proof of the method's correctness. Experiments shed light on its efficiency and practicality for large-scale Bayesian networks and databases.
- R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. Verkamo. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, 1996.]] Google ScholarDigital Library
- R. Bayardo and R. Agrawal. Mining the most interesting rules. In Proceedings of the SIGKDD Conference on Knowledge Discovery and Data Mining, 1999.]] Google ScholarDigital Library
- H. Dodge and H. Romig. A method of sampling inspection. The Bell System Technical Journal, 8:613--631, 1929.]]Google ScholarCross Ref
- C. Domingo, R. Gavalda, and O. Watanabe. Adaptive sampling methods for scaling up knowledge discovery algorithms. Data Mining and Knowledge Discovery, 6(2):131--152, 2002.]] Google ScholarDigital Library
- U. Fayyad, G. Piatetski-Shapiro, and P. Smyth. Knowledge discovery and data mining: Towards a unifying framework. In KDD-96, 1996.]]Google Scholar
- W. Gilks, S. Richardson, and D. Spiegelhalter, editors. Markov Chain Monte Carlo in Practice. Chapman & Hall, 1995.]]Google ScholarCross Ref
- R. Greiner. PALO: A probabilistic hill-climbing algorithm. Artificial Intelligence, 83(1--2), July 1996.]] Google ScholarDigital Library
- G. Hulten and P. Domingos. Mining complex models from aribtrarily large datasets in constant time. In Proceedings of the SIGKDD Conference on Knowledge Discovery and Data Mining, 2002.]] Google ScholarDigital Library
- S. Jaroszewicz and D. Simovici. A general measure of rule interestingness. In Proceedings of the European Conference on Principles and Practice of Knowledge Discovery and Data Mining, 2001.]] Google ScholarDigital Library
- S. Jaroszewicz and D. Simovici. Interestingness of frequent itemsets using Bayesian networks as background knowledge. In Proceedings of the SIGKDD Conference on Knowledge Discovery and Data Mining, 2004.]] Google ScholarDigital Library
- F. Jensen. Bayesian Networks and Decision Graphs. Springer Verlag, 2001.]] Google ScholarDigital Library
- W. Klösgen. Assistant for knowledge discovery in data. In P. Hoschka, editor, Assisting Computer: A New Generation of Support Systems, 1995.]]Google Scholar
- R. Kruse. Knowledge-based operations on graphical models. In Proceedings of the Dagstuhl Seminar on Probabilistic, Logical, and Relational Learning, 2005. In print.]]Google Scholar
- O. Maron and A. Moore. Hoeffding races: Accelerating model selection search for classification and function approximating. In Advances in Neural Information Processing Systems, pages 59--66, 1994.]]Google Scholar
- B. Padmanabhan and A. Tuzhilin. Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems, 27(3):303--318, 1999.]] Google ScholarDigital Library
- B. Padmanabhan and A. Tuzhilin. Small is beautiful: discovering the minimal set of unexpected patterns. In Proceedings of the Sixth SIGKDD Conference on Knowledge Discovery and Data Mining, 2000.]] Google ScholarDigital Library
- P. Myllymäki, T. Silander, H. Tirri, and P. Uronen. B-course: A web-based tool for bayesian and causal data analysis. International Journal on Artificial Intelligence Tools, 11(3):369--387, 2002.]]Google ScholarCross Ref
- T. Scheffer. Finding association rules that trade support optimally against confidence. In Proceedings of the European Conference on Principles and Practice of Knowledge Discovery in Databases, 2001.]] Google ScholarDigital Library
- T. Scheffer and S. Wrobel. Finding the most interesting patterns in a database quickly by using sequential sampling. Journal of Machine Learning Research, 3:833--862, 2002.]] Google ScholarDigital Library
- A. Silberschatz and A. Tuzhilin. On subjective measures of interestingness in knowledge discovery. In Proceedings of the SIGKDD Conference on Knowledge Discovery and Data Mining, 1995.]]Google Scholar
- H. Toivonen. Sampling large databases for association rules. In Proceedings of the International Conference on Very Large Databases, 1996.]] Google ScholarDigital Library
Index Terms
- Fast discovery of unexpected patterns in data, relative to a Bayesian network
Recommendations
On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery
A drawback of traditional data-mining methods is that they do not leverage prior knowledge of users. In prior work, we proposed a method that could discover unexpected patterns in data by using domain knowledge in a systematic manner. In this paper, we ...
Human disease network guided discovery of interesting itemsets in hospital discharge data
DMMH '11: Proceedings of the 2011 workshop on Data mining for medicine and healthcareStandard knowledge discovery techniques, such as unsupervised or supervised descriptive rule discovery, have been widely used in medical data mining. Most of the research is focused on developing effective association rule evaluation metrics that would ...
Comments