Efficiently mining long patterns from databases

Efficiently mining long patterns from databases (1998)

by Roberto J. Bayardo

Citations:

324 - 3 self

Datum	Value	Source
TITLE	Efficiently mining long patterns from databases	INFERENCE
AUTHOR NAME	Roberto J. Bayardo	SVM HeaderParse 0.2
AUTHOR AFFIL	IBM Almaden Research Center	SVM HeaderParse 0.2
ABSTRACT	We present a pattern-mining algorithm that scales roughly linearly in the number of maximal patterns embedded in a database irrespective of the length of the longest pattern. In comparison, previous algorithms based on Apriori scale exponentially with longest pattern length. Experiments on real data show that when the patterns are long, our algorithm is more efficient by an order of magnimaximal frequent itemset, Max-Miner’s output implicitly and concisely represents all frequent itemsets. Max-Miner is shown to result in two or more orders of magnitude in performance improvements over Apriori on some data-sets. On other data-sets where the patterns are not so long, the gains are more modest. In practice, Max-Miner is demonstrated to run in time that is roughly linear in the number of maximal frequent itemsets and the size of the database, irrespective of the size of the longest frequent itemset. tude or more. 1.	SVM HeaderParse 0.2
YEAR	1998	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	85--93	INFERENCE
CITATIONS	13 found	ParsCit 1.0