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 …

Big data analytics are gaining popularity in medical engineering and healthcare use cases.
Stakeholders are finding big data analytics reduce medical costs and personalise medical …

Many techniques have been proposed to implement the Apriori algorithm on MapReduce
framework but only a few have focused on performance improvement. FPC (Fixed Passes …

Data management and processing to enable predictive analytics in cyber physical systems
holds the promise of creating insight over underlying processes, discovering anomalous …

In this paper we present a new conceptual model of trajectories, which accounts for
semantic and indoor space information and supports the design and implementation of …

Top-k frequent itemset mining (top-k FIM) plays an important role in many practical
applications. It reports the k itemsets with the highest supports. Rather than the subtle …

The recent expansion of IoT-enabled (Internet of Things) devices in manufacturing contexts
and their subsequent data-driven exploitation paved the way to the advent of the Industry …

This paper considers frequent itemsets mining in transactional databases. It introduces a
new accurate single scan approach for frequent itemset mining (SSFIM), a heuristic as an …

Frequent itemset mining (FIM) is a common approach for discovering hidden frequent
patterns from transactional databases used in prediction, association rules, classification …

Current advances in the Internet of Things (IoT) and Cloud involve the presence of an
additional layer between them acting as mediator for data transfer and processing in close …