Clustering and Approximate Identification of Frequent Item Sets
نویسندگان
چکیده
We propose an algorithm that computes an approximation of the set of frequent item sets by using the bit sequence representation of the associations between items and transactions. The algorithm is obtained by modifying a hierarchical agglomerative clustering algorithm and takes advantage of the speed that bit operations afford. The algorithm offers a very significant speed advantage over standard implementations of the Apriori technique and, under certain conditions, recovers the preponderant part of the frequent item sets.
منابع مشابه
New algorithms for finding approximate frequent item sets
In standard frequent item set mining a transaction supports an item set only if all items in the set are present. However, in many cases this is too strict a requirement that can render it impossible to find certain relevant groups of items. By relaxing the support definition, allowing for some items of a given set to be missing from a transaction, this drawback can be amended. The resulting it...
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