The main purpose of data mining and analytics is to find novel, potentially useful patterns
that can be utilized in real-world applications to derive beneficial knowledge. For identifying …

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 …

Abstract Association Rule Mining (ARM) is a significant task for discovering frequent patterns
in data mining. It has achieved great success in a plethora of applications such as market …

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 …

Data mining plays an important role in various human activities because it extracts the
unknown useful patterns (or knowledge). Due to its capabilities, data mining become an …

As the world becomes increasingly connected, it is also more exposed to a myriad of cyber
threats. We need to use multiple types of tools and techniques to learn and understand the …

This paper addresses the problem of responding to user queries by fetching the most
relevant object from a clustered set of objects. It addresses the common drawbacks of cluster …

Association rule mining (ARM) is defined by its crucial role in finding common pattern in data
mining. It has different types such as fuzzy, binary, numerical. In this paper, we introduce a …

The aim of sequential pattern mining (SPM) is to discover potentially useful information from
a given sequence. Although various SPM methods have been investigated, most of these …

Analyzing sequence data usually leads to the discovery of interesting patterns and then
anomaly detection. In recent years, numerous frameworks and methods have been …