Frequent itemset mining (FIM) is an essential task within data analysis since it is responsible
for extracting frequently occurring events, patterns, or items in data. Insights from such …

Frequent itemset mining (FIM) is a useful tool for discovering frequently co-occurrent items.
Since its inception, a number of significant FIM algorithms have been developed to speed up …

Searching frequent patterns in transactional databases is considered as one of the most
important data mining problems and Apriori is one of the typical algorithms for this task …

As we are living in the era of big data, huge volumes of a wide variety of complex data-which
can be of different levels of veracity-are generated or collected at a high velocity from rich …

Associative classifiers have proven to be very effective in classification problems.
Unfortunately, the algorithms used for learning these classifiers are not able to adequately …

Frequent itemset mining (FIM) plays an essential role in mining associations, correlations
and many other important data mining tasks. Unfortunately, as the volume of dataset gets …

The latest techniques and principles of parallel and grid database processing The growth in
grid databases, coupled with the utility of parallel query processing, presents an important …

Extraction of valuable data from extensive datasets is a standout amongst the most vital
exploration issues. Association rule mining is one of the highly used methods for this …

Privacy and security risks arising from the application of different data mining techniques to
large institutional data repositories have been solely investigated by a new research …

Multi-core processors are proliferated across different domains in recent years. In this paper,
we study the performance of frequent pattern mining on a modern multi-core machine. A …