Sequential All Frequent Itemsets Detection
Konstantinos F. Xylogiannopoulos, Panagiotis Karampelas, Reda Alhajj · 2015
Sequential frequent itemsets detection is one of the core problems in data mining. In the current paper we propose a new methodology based on our previous work regarding the detection of all repeated patterns in a string. By analyzing big datasets from FIMI website of up to one million transactions we were able to detect not only the most frequent sequential itemsets but any sequential itemset occurred at least twice in the transactions' database. For this purpose we have used a novel data structure the LERP Reduced Suffix Array and the innovative ARPaD algorithm which allows the detection of all repeated patterns in a string. The methodology uses a pre-statistical analysis of the transactions that allows constructing in a very efficient way smaller LERP-RSA data structures for each transaction. The integration and classification of all LERP-RSAs let ARPaD algorithm to be executed in parallel and to detect every sequential itemset that occurs at least twice in a very efficient way.