Efficient Algorithms for Mining Top-K High Utility Itemsets

ieee projects 2016 Tanjore(Thanjavur), Pudukkottai High utility item sets (HUIs) mining is an emerging topic in data mining, which refers to discovering all item-sets 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 item-set mining, where k is the desired number of HUIs to be mined. Two types of efficient algorithms named TKU (mining Top-K Utility item sets) and TKO (mining Top-K utility item sets in One phase) are proposed for mining such item-sets 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 data-sets show that the performance of the proposed algorithms is close to that of the optimal case of state-of-the-art utility mining algorithms.