Drawing a discriminative pattern in quantitative datasets is often represented to return high utility (HUP). The traditional methods output patterns with above pre-given threshold. Nevertheless, the current user-centered algorithm requires outputting results timely manner strengthen interaction between mining system and users. Pattern sampling can probability guarantee short time, it could be c...

This thesis is basically designed for privacy preserving utility mining using sanitization approach. In this work itemsets are provided safety using an approach , firstly we will calculate the utility of all itemsets as the product of item cost and its number of transactions, then we will set a threshold utility which will be the average of max and min utility. Now, we will try to reduce the di...

Discovering high-utility temsets in transaction databases is a popular data mining task. A limitation of traditional algorithms is that a huge amount of high-utility itemsets may be presented to the user. To provide a concise and lossless representation of results to the user, the concept of closed high-utility itemsets was proposed. However, mining closed high-utility itemsets is computational...

Received Jan 14, 2017 Revised Jun 7, 2017 Accepted Sep 11, 2017 Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a w...

High-utility sequential pattern mining (HUSPM) has recently emerged as a focus of intense research interest. The main task HUSPM is to find all subsequences, within quantitative database, that have high utility with respect user-defined minimum threshold. However, it difficult specify the threshold, especially when database features, which are invisible in most cases, not understood. To handle ...

In recent years, extensive studies have been conducted on high utility itemsets (HUI) mining with wide applications. However, most of them assume that data are stored in centralized databases with a single machine performing the mining tasks. Consequently, existing algorithms cannot be applied to the big data environments, where data are often distributed and too large to be dealt with by a sin...

The major challenge of data perturbation is to achieve the desired balance between the level of privacy guarantee and the level of data utility. Data privacy and data utility are commonly considered as a pair of conflicting requirements in privacy-preserving data mining systems and applications. Multiplicative perturbation algorithms aim at improving data privacy while maintaining the desired l...