Efficient algorithm for mining high average-utility itemsets in incremental transaction databases

Abstract

In this paper, we present a novel algorithm for efficiently mining high average-utility itemsets (HAUIs) from incremental databases, in which their volumes can be expanded dynamically. The previous algorithms have inefficiencies in that they must scan a given database multiple times so as to generate candidate itemsets and determine valid itemsets level by level. The reason is that they follow the basic framework of an Apriori-like approach. This drawback can cause critical problems in processing incremental databases because scanning a database becomes a tougher task as the size of the database is increased. In contrast, the algorithm proposed in this paper builds a compact tree structure maintaining all necessary information in order to avoid such excessive database scanning during its mining process. The previous algorithms suffer from the huge generation of unnecessary candidate itemsets at each level accompanied by the naive combination based candidate generation manner of an Apriori-like approach, which generates candidate itemsets with (k+1)-lengths by simply joining itemsets with k-lengths. On the other hand, our algorithm employs the pattern growth approach, which allows the algorithm to generate a set of only essential candidate itemsets. In order for our algorithm to constantly preserve the compactness of its tree structure during the entire incremental mining process, a restructuring technique is exploited. In the performance evaluation, we show that our algorithm is faster and consumes less memory space than competitors.