Tractable Learning of Large Bayes Net Structures from Sparse Data

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Tractable Learning of Large Bayes Net Structures from Sparse Data (2004)

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by Anna Goldenberg

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@INPROCEEDINGS{Goldenberg04tractablelearning,
    author = {Anna Goldenberg},
    title = {Tractable Learning of Large Bayes Net Structures from Sparse Data},
    booktitle = {},
    year = {2004},
    publisher = {ACM Press}
}

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Abstract

This paper addresses three questions. Is it useful to attempt to learn a Bayesian network structure with hundreds of thousands of nodes? How should such structure search proceed practically? The third question arises out of our approach to the second: how can Frequent Sets (Agrawal et al., 1993), which are extremely popular in the area of descriptive data mining, be turned into a probabilistic model? Large sparse datasets with hundreds of thousands of records and attributes appear in social networks, warehousing, supermarket transactions and web logs. The complexity of structural search made learning of factored probabilistic models on such datasets unfeasible. We propose to use Frequent Sets to significantly speed up the structural search. Unlike previous approaches, we not only cache n-way sufficient statistics, but also exploit their local structure.

Keyphrases

tractable learning large bayes net sparse data frequent set structural search n-way sufficient statistic bayesian network structure probabilistic model third question social network supermarket transaction factored probabilistic model descriptive data mining previous approach structure search local structure large sparse datasets web log

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