Integration of Dataset Scans in Processing Sets of Frequent Itemset Queries
作者： Marek Wojciechowski , Maciej Zakrzewicz , Pawel Boinski
DOI: 10.1007/978-3-642-23166-7_9
关键词: Set (abstract data type) 、 Data mining 、 Hypergraph 、 Domain (software engineering) 、 Association rule learning 、 Computer science 、 A priori and a posteriori 、 Arbitrarily large 、 Constraint (information theory)
摘要: Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using given constraint model. In this chapter we address problem of processing sets frequent queries, which brings ideas multiple-query optimization to domain data mining. The most attractive method solving with respect possible practical applications Common Counting consists in concurrent execution queries Apriori integration scans parts database shared among queries. major advantage over its alternatives applicability arbitrarily large batches If memory structures all be processed by do not fit together main memory, set has partitioned into subsets several phases. We formalize dividing for specific case hypergraph partitioning provide comprehensive overview query algorithms proposed so far.
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