Abstract
Estimating joint probabilities plays an important role in many data mining and machine learning tasks. In this paper we introduce two methods, minAB and prodAB, to estimate joint probabilities. Both methods are based on a light-weight structure, partition support. The core idea is to maintain the partition support of itemsets over logically disjoint partitions and then use it to estimate joint probabilities of itemsets of higher cardinalitiess. We present extensive mathematical analyses on both methods and compare their performances on synthetic datasets. We also demonstrate a case study of using the estimation methods in Apriori algorithm for fast association mining. Moreover, we explore the usefulness of the estimation methods in other mining/learning tasks [9]. Experimental results show the effectiveness of the estimation methods.
The project is supported in part by NIH Grants 5-P41-RR09283, RO1-AG18231, and P30-AG18254 and by NSF Grants EIA-0080124, NSF CCR-9701911, and DUE- 9980943. We would also like to thank Dr. Meng Xiang Tang and Xianghui Liu for their helpful discussions.
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Li, T., Zhu, S., Ogihara, M., Cheng, Y. (2002). Estimating Joint Probabilities from Marginal Ones* . In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2002. Lecture Notes in Computer Science, vol 2454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46145-0_4
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DOI: https://doi.org/10.1007/3-540-46145-0_4
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