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 …

Any paper proposing a new algorithm should come with an evaluation of efficiency and
scalability (particularly when we are designing methods for “big data”). However, there are …

In the current era of automation, machines are constantly being channelized to provide
accurate interpretations of what people express on social media. The human race nowadays …

Mining frequent itemsets and association rules is a popular and well researched approach
for discovering interesting relationships between variables in large databases. The R …

Efficient algorithms for mining frequent itemsets are crucial for mining association rules as
well as for many other data mining tasks. Methods for mining frequent itemsets have been …

The FP-growth algorithm is currently one of the fastest approaches to frequent item set
mining. In this paper I describe a C implementation of this algorithm, which contains two …

Frequent item set mining is one of the best known and most popular data mining methods.
Originally developed for market basket analysis, it is used nowadays for almost any task that …

Recommender systems have been extensively studied to present items, such as movies,
music and books that are likely of interest to the user. Researchers have indicated that …

Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon
methodologies for extracting useful knowledge from data and there are several useful KDD …

Vulnerabilities in common security components such as firewalls are inevitable. Intrusion
Detection Systems (IDS) are used as another wall to protect computer systems and to …