Fast Identification of High Utility Itemsets from Candidates

Abstract

High utility itemsets (HUIs) are sets of items with high utility, like profit, in a database. Efficient mining of high utility itemsets is an important problem in the data mining area. Many mining algorithms adopt a two-phase framework. They first generate a set of candidate itemsets by roughly overestimating the utilities of all itemsets in a database, and subsequently compute the exact utility of each candidate to identify HUIs. Therefore, the major costs in these algorithms come from candidate generation and utility computation. Previous works mainly focus on how to reduce the number of candidates, without dedicating much attention to utility computation, to the best of our knowledge. However, we find that, for a mining task, the time of utility computation in two-phase algorithms dominates the whole running time of these algorithms. Therefore, it is important to optimize utility computation. In this paper, we first give a basic algorithm for HUI identification, the core of which is a utility computation procedure. Subsequently, a novel candidate tree structure is proposed for storing candidate itemsets, and a candidate tree-based algorithm is developed for fast HUI identification, in which there is an efficient utility computation procedure. Extensive experimental results show that the candidate tree-based algorithm outperforms the basic algorithm and the performance of two-phase algorithms, integrating the candidate tree algorithm as their second step, can be significantly improved.

Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

“…Most researchers have been focusing on how to eliminate redundant candidates during the process of mining HUIs; however, the time needed to compute the utility value of itemsets is a significant part of the whole running time. To reduce the long runtime of utility computation, Qu et al [26] thus presented the basic identification algorithm (BIA) for mining HUIs. They then proposed a candidate tree with a novel structure and based on this developed a candidate tree-based algorithm called the fast identification algorithm (FIA) to quickly identify HUIs.…”

“…Itemset is used; if R has a utility confidence value (R.uconf ) greater than or equal to min-uconf (line 24), R is added into the results (line [25][26][27]. If R is valid, Q is enqueued in the list of child nodes of L i (lines 32-37).…”

An Efficient Method for Mining Closed Potential High-Utility Itemsets

“…Later, Tseng et al [5] proposed an improved version of UP-Growth, called UP-Growth+, which reduces overestimated utilities. To minimize the cost of candidate generation and utility calculation from two-phase HUIM algorithms, Qu et al [25] proposed a novel tree structure for storing candidates and a tree-based algorithm to quickly identify HUIs. Experimental results showed that the proposed candidate tree structure and the algorithm outperforms the performance of two-phase algorithms.…”