Analyzing customer transactions to discover high-utility itemsets is a popular task, which
consists of finding the sets of items that are purchased together and yield a high profit …

Data mining techniques are intensively used to uncover relevant patterns in large volumes
of complex data which are continuously extended with newly arrived data instances …

The correlation analysis of commodity sales is very important in cross-marketing. A means of
undertaking dynamic-model-based time series data-mining was proposed to analyze the …

We are currently seeing a data explosion and witnessing a meteoric rise in database sizes;
therefore, with the widespread availability of such large volumes of data, there has arisen an …

Association rule mining is one of the main means in Knowledge discovery and Machine
learning. Such kind of rules present knowledge of interrelations among items in a dataset …

To create a classifier using an associative classification algorithm, a complete set of class
association rules (CARs) is obtained from the training dataset. Most generated rules …

Mining class association rules (CARs) with the itemset constraint is concerned with the
discovery of rules, which contain a set of specific items in the rule antecedent and a class …

Classification is a very important task in data mining and pattern analysis, which have been
widely used to solve various real-world problems. To obtain better classification …

The problem of mining frequent sequential patterns (FSPs) has attracted a great deal of
research attention. Although there are many efficient algorithms for mining FSPs, the mining …

Class association rules (CARs) are basically used to build a classification model for
prediction; they can also be used to describe correlations between itemsets and class …