Abstract Machine Learning (ML) and Data Mining (DM) build tools intended to help users
solve data‐related problems that are infeasible for “unaugmented” humans. Tools need …

Because of the rapid growth in worldwide information, efficiency of association rules mining
(ARM) has been concerned for several years. In this paper, based on the original Apriori …

Traditional frequent pattern mining methods consider an equal profit/weight for all items and
only binary occurrences (0/1) of the items in transactions. High utility pattern mining …

Social media platforms have become fundamental tools for sharing information during
natural disasters or catastrophic events. This paper presents SEDOM-DD (Sub-Events …

The analytical process designed to mine data became more difficult with the rapid
information explosion. This in-turn created completely distributed and un-indexed data. Thus …

Computer science is essentially an applied or engineering science, creating tools. In Data
Mining, those tools are supposed to help humans understand large amounts of data. In this …

Ever growing size of information and database has always demanded the scientific world for
very efficient rule mining algorithm. This paper gives an extension to the Apriori algorithm, a …

The itemset tree data structure is used in targeted association mining to find rules within a
user's sphere of interest. In this paper, we propose two enhancements to the original …

The goal of association mining is to find potentially interesting rules in large repositories of
data. Unfortunately using a minimum support threshold, a standard practice to improve the …

Big data analytics examines large amounts of data to uncover hidden patterns, correlations
and other insights. In this work, a novel association rule-mining algorithm is employed for …