Fast, Simple and Memory Efficient Algorithm for Mining Association Rules
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One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process is the computation of the frequency of the occurrences of interesting subset of items (called candidates) in the database of transactions. In this paper, we introduce algorithms that overcome major drawbacks of previous work. First, for small-scale database, both the processing time has been improved significantly. Second, for large-scale database, the proposed algorithms can deal with a database that is substantially larger than the size of available memory. The contributions made in this paper are particularly important because the rate of increase in database size and response time requirements has out-paced advancements in processor and mass storage technology.
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