Associative classification (AC) integrates the task of mining association rules with the
classification task to increase the efficiency of the classification process. AC algorithms …

Most businesses focus on the profits. For example, supermarkets often analyze sale
activities to investigate which products bring the most revenue, as well as find out customer …

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 …

Associative classification (AC) is an integration between association rules and classification
tasks that aim to predict unseen samples. Several studies indicate that the AC algorithms …

Cancer is a worldwide problem and one of the leading causes of death. Increasing
prevalence of cancer, particularly in developing countries, demands better understandings …

HUIM (High utility itemsets mining) is a sub-division of data mining dealing with the task to
obtain promising patterns in the quantitative datasets. A variant of HUIM is to discover the …

Mining of colossal patterns is used to mine patterns in databases with many attributes and
values, but the number of instances in each database is small. Although many efficient …

In this age of automation, Machine learning (ML) plays the main role in agriculture sector to
suggest suitable advice, crop advice, which includes decisions of growing crops, and advice …

Importance of methods for explainable AI has been ever growing within many processes; a
simple form of knowledge is needed by humans who operate the processes. One of the …

Frequent itemset mining (FIM) is a fundamental set of techniques used to discover useful
and meaningful relationships between items in transaction databases. In recent decades …