Incident investigation reports provide information on defects related to the system safety and
indications for improvements. Currently, the analysis of these reports relies heavily on …

As one of the important approaches in privacy preserving data mining, privacy preserving
utility mining has been studied to find more meaningful results while database privacy is …

Data mining is essentially applied to discover new knowledge from a database through an
iterative process. The mining process may be time consuming for massive datasets. A widely …

Many algorithms have been proposed to find high utility itemsets (sets of items that yield a
high profit) in customer transactions. Though, it is useful to analyze customer behavior, it …

High utility itemset mining is a well-studied data mining task for analyzing customer
transactions. The goal is to find all high utility itemsets, that is items purchased together that …

HUIM (High utility itemsets mining) is a sub-division of data mining dealing with the task to
obtain promising patterns in the quantitative datasets. A variant of HUIM is to discover the …

Fast discovery of association rules from millions of transactions in a variety of large
databases has now become a major challenge in data mining domain. Frequent itemsets …

Closed itemsets and their generators play an important role in frequent itemset and
association rule mining. They allow a lossless representation of all frequent itemsets and …

Change mining is one of the main subjects of analysis on time-evolving data. Regardless of
the distribution of the changes over the data, often the algorithms return very large sets of …

A challenge in association rules' mining is effectively reducing the time and space
complexity in association rules mining with predefined minimum support and confidence …