Discovering High Utility Itemsets using Hybrid Approach

Mining of high utility itemsets especially from the big transactional databases is time consuming task. For mining the high utility itemsets from large transactional datasets multiple methods are available and have some consequential limitations. In case of performance these methods need to be scrutinized under low memory based systems for mining high utility itemsets from transactional datasets as well as to address further measures. The proposed algorithm combines the High Utility Pattern Mining and Incremental Frequent Pattern Mining. Two algorithms used are Apriori and existing Parallel UP Growth for mining high utility itemsets using transactional databases. The information about high utility itemsets is maintained in a data structure called UP tree. These algorithms are not only used to scans the incremental database but also collects newly generated frequent itemsets support count. It provides fast execution because it includes new itemsets in tree and removes rare itemset from a utility pattern tree structure that reduces cost and time. From various Experimental analysis and results, this hybrid approach with existing Apriori and UP-Growth is proposed with aim of improving the performance

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

Efficient chain structure for high-utility sequential pattern mining

High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns (HUSPs) from the quantitative databases. Several works have been presented to reduce the computational cost by variants of pruning strategies. In this paper, we present an efficient sequence-utility (SU)-chain structure, which can be used to store more relevant information to improve mining performance. Based on the SU-Chain structure, the existing pruning strategies can also be utilized here to early prune the unpromising candidates and obtain the satisfied HUSPs. Experiments are then compared with the state-of-the-art HUSPM algorithms and the results showed that the SU-Chain-based model can efficiently improve the efficiency performance than the existing HUSPM algorithms in terms of runtime and number of the determined candidates