Efficient Algorithms for Mining Top-K High Utility Itemsets

Abstract

High utility itemsets (HUIs) mining is an emerging topic in data mining, which refers to discovering all itemsets having a utility meeting a user-specified minimum utility threshold min_util. However, setting min_util appropriately is a difficult problem for users. Generally speaking, finding an appropriate minimum utility threshold by trial and error is a tedious process for users. If min_util is set too low, too many HUIs will be generated, which may cause the mining process to be very inefficient. On the other hand, if min_util is set too high, it is likely that no HUIs will be found. In this paper, we address the above issues by proposing a new framework for top-k high utility itemset mining, where k is the desired number of HUIs to be mined. Two types of efficient algorithms named TKU (mining Top-K Utility itemsets) and TKO (mining Top-K utility itemsets in One phase) are proposed for mining such itemsets without the need to set min_util. We provide a structural comparison of the two algorithms with discussions on their advantages and limitations. Empirical evaluations on both real and synthetic datasets show that the performance of the proposed algorithms is close to that of the optimal case of state-of-the-art utility mining algorithms.

Mining High Utility Itemsets with Regular Occurrence

“…Negative unit profits of items are addressed in [15,16]. To avoid difficulties in setting a proper utility threshold, in [17][18][19] attempts were done to mine a set of itemsets with the highest utility. Podpecan [20] and Sugunadevi [21] proposed to discover high utility-frequent itemsets based on consideration of utility and frequency of occurrence.…”

A Better Approach for Mining High Utility Itemset from Transactional Databases

“…Itemset List Is Generated As Follows: [9], [10] is one of the efficient algorithms to generate high utility itemsets depending on construction of a global UP-Tree. The tree maintains the information about the itemsets and their utilities.…”

Recommendation System for High Utility Itemsets over Incremental Dataset

“…Such a large number of HTWUI"s will degrade the mining performance in phase I substantially in terms of execution time and memory consumption. As advancement, Tseng, Wu, Shie & Yu [27] proposed UP-Growth for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. Correspondingly, a compact tree structure, called UP-Tree (Utility Pattern Tree), was designed to maintain the important information of the transaction database related to the utility patterns.An incremental mining algorithm FUUP tree [31] for efficiently mining high utility itemsets is proposed to handle dynamic datasets.…”

“…Utility mining [5,29,30] was proposed to solve the above mentioned problem by considering the factors like cost, profit or other factors of users" interest. Thus the issue of high utility itemsets mining is raised and many studies [4,9,17,19,20,25,27] have addressed this problem. Liu, Liao & Choudhary [19,20] proposed the two phase utility mining algorithm for efficiently extracting all high utility itemsets based on the downward closure property.…”

“…To efficiently generate HTWI"s and to avoid multiple scans, Ahmed, Tanbeer, Jeong & Lee [4] proposed a tree based algorithm named IHUP. More recently, Tseng, Wu, Shie & Yu [27] proposed UP-Growth for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets.…”