Association rule mining is a common technique used in discovering interesting frequent patterns in data acquired in various application domains. The search space combinatorically explodes as the size of the data increases. Furthermore, the introduction of new data can invalidate old frequent patterns and introduce new ones. Hence, while finding the association rules efficiently is an important problem, maintaining and updating them is also crucial. Several algorithms have been introduced to find the association rules efficiently. One of them is Apriori. There are also algorithms written to update or maintain the existing association rules. Update with early pruning (UWEP) is one such algorithm. In this paper, the authors propose that in certain conditions it is preferable to use an incremental algorithm as opposed to the classic Apriori algorithm. They also propose new implementation techniques and improvements to the original UWEP paper in an algorithm we call UWEP2. These include the use of memorization and lazy evaluation to reduce scans of the dataset.
In this study we implemented four different versions of Apriori, namely, basic and basic multi-threaded, bloom filter, trie, and count-min sketch, and proposed a new algorithm – NCLAT (Near Candidate-Less Apriori with Tidlists). We compared the runtimes and max memory usages of our implementations among each other as well as with the runtime of Borgelt’s Apriori implementation in some of the cases. NCLAT implementation is more efficient than the other Apriori implementations that we know of in terms of the number of times the database is scanned, and the number of candidates generated. Unlike the original Apriori algorithm which scans the database for every level and creates all of the candidates in advance for each level, NCLAT scans the database only once and creates candidate itemsets only for level one but not afterwards. Thus the number of candidates created is equal to the number of unique items in the database.
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