TKN: An efficient approach for discovering top-k high utility itemsets with positive or negative profits

Abstract

Top-k high utility itemsets (HUIs) mining permits discovering the required number of patterns - k, without having an optimal minimum utility threshold (i.e., minimum profit). Multiple top-k HUIs mining algorithms have been introduced with interesting results. However, these algorithms focus mainly on mining patterns from positive profit datasets, while few preliminary studies can handle datasets with negative profits. Moreover, conventional top-k HUI mining algorithms, that are meant for exploring positive profit datasets, perform poorly when mining top-k HUIs on highly dense and large datasets. In this paper, we propose TKN (efficiently mining Top-K HUIs with positive or Negative profits) which employs generalized and adaptive techniques to mine both positive and negative profit datasets effectively. The proposed approach adopts transactional projection and merging mechanisms to decrease the dataset traversing cost. Furthermore, several pruning and threshold elevating ideas are utilized to significantly narrow the exploration space. To highlight the reliability of the devised TKN, a series of extensive comparisons were conducted using two versions of six real datasets. The obtained results reveal that TKN is clearly superior in finding the required number of patterns, whether on positive or negative profit datasets, compared to the current cutting-edge competitors.