Abstract The High Average Utility Itemset Mining (HAUIM) technique, a variation of High
Utility Itemset Mining (HUIM), uses the average utility of the itemsets. Historically, most …

Supervised classification based on Contrast Patterns (CP) is a trending topic in the pattern
recognition literature, partly because it contains an important family of both understandable …

Erasable pattern mining is one of the important fields of frequent pattern mining. It diagnoses
and solves the economic problems that arise in the manufacturing industry. The real-world …

The emergence of mobile crowdsensing (MCS) has provided us with unprecedented
opportunities for both sensing coverage and data transmission. However, in many MCS …

Frequent weighted itemsets (FWIs) are a variation of frequent itemsets (FIs) that take into
account the different importance or weights for each item. Many algorithms have been …

Mining frequent patterns (FP) is an essential task in data mining. The parameter required for
this task is typically the minimum support threshold. Tuning this parameter to a suitable …

High-accuracy real-time data stream estimations are critical for various applications, and
sliding-window-based techniques have attracted wide attention. However, existing solutions …

Abstract Top-K Uncertain Frequent Pattern (UFP) mining is an interesting topic in data
mining. The existing TUFP algorithm supports static mining of Top-K UFPs; however, in the …

In industrial areas, most of databases are dynamic databases, and the volume of the
databases has grown with the passage of time. Especially, pattern mining for incremental …

This paper studies a new but important pattern for items in data streams, called promising
items. The promising items mean that the frequencies of an item in multiple continuous time …