Discovering unexpected and useful patterns in databases is a fundamental data mining task.
In recent years, a trend in data mining has been to design algorithms for discovering …

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 …

This chapter will provide a detailed survey of frequent pattern mining algorithms. A wide
variety of algorithms will be covered starting from Apriori. Many algorithms such as Eclat …

Itemset mining is an important subfield of data mining, which consists of discovering
interesting and useful patterns in transaction databases. The traditional task of frequent …

In recent years, high-utility itemset mining has emerged as an important data mining task.
However, it remains computationally expensive both in terms of runtime and memory …

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 …

A common method: build a predictive model, eg, AR, LDS, RNN models: x (t)= f (x {t− p: t− 1},
u {t− p: t− 1}; θ)► x {t− p: t− 1} the sensor measurements from time t− p to t− 1► u {t− p: t− 1} …

High-utility itemset mining (HUIM) is an important data mining task with wide applications. In
this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining) …

Compared to other anomalous video event detection approaches that analyze object
trajectories only, we propose a context-aware method to detect anomalies. By tracking all …

Maximal bicliques are effective to reveal meaningful information hidden in bipartite graphs.
Maximal biclique enumeration (MBE) is challenging since the number of the maximal …