11,382 research outputs found
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
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