document: Inverted matrix : efficient discovery of frequent items in large datasets in the context of interactive mining Existing association rule mining algorithms suffer from many problems when mining massive transactional datasets . One major problem is the high memory dependency : either the gigantic data structure built is assumed to fit in main memory , or the recursive mining process is too voracious in memory resources . Another major impediment is the repetitive and interactive nature of any knowledge discovery process . To tune parameters , many runs of the same algorithms are necessary leading to the building of these huge data structures time and again . This paper proposes a new disk-based association rule mining algorithm called Inverted Matrix , which achieves its efficiency by applying three new ideas . First , transactional data is converted into a new database layout called Inverted Matrix that prevents multiple scanning of the database during the mining phase , in which finding frequent patterns could be achieved in less than a full scan with random access . Second , for each frequent item , a relatively small independent tree is built summarizing co-occurrences . Finally , a simple and non-recursive mining process reduces the memory requirements as minimum candidacy generation and counting is needed . Experimental studies reveal that our Inverted Matrix approach outperform FP-Tree especially in mining very large transactional databases with a very large number of unique items . Our random access disk-based approach is particularly advantageous in a repetitive and interactive setting .
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