Because of the billions of transactions that take place every day, the volumes of information
is always growing. There are a plethora of categorization techniques accessible for …

Classification is an important data mining task which facilitates list of decision rules that
helps us to predict class of an unseen instance. Various traditional techniques like Decision …

Nature inspired algorithms have become popular for discovering classification rules due to
their ability to effectively handle large and complex search spaces. However, nature inspired …

Fuzzy rule-based systems (FRBSs) are proficient in dealing with cognitive uncertainties like
vagueness and ambiguity imperative to real-world decision-making situations. Fuzzy …

Feature selection is a pre-processing technique in which a subset or a small number of
features, which are relevant and non-redundant, are selected for better classification …

Ant colony optimisation (ACO) algorithms have been used to discover accurate, and
comprehensible classification rules. Discovering exceptions using ACO is an underexplored …

One of the vital areas of attention in the field of knowledge discovery is to analyze the
interestingness measures in rule discovery and to select the best one according to the …

Decision trees are the most admired and extensively used classification algorithms in data
mining. These are considered accurate, easy to use, and comprehensible classifiers. Like …

Decision tree algorithms, being accurate and comprehensible classifiers, have been one of
the most widely used classifiers in data mining and machine learning. However, like many …

Most of the classification algorithms discover flat Fuzzy Classification Rules (FCRs) in 'If-
Then'form. The knowledge discovered in the form of FCRs allows us to deal with vague …