Classification of data with imbalanced class distribution has encountered a significant
drawback of the performance attainable by most standard classifier learning algorithms …

Associative classification mining is a promising approach in data mining that utilizes the
association rule discovery techniques to construct classification systems, also known as …

We present a machine learning algorithm for building classifiers that are comprised of a
small number of short rules. These are restricted disjunctive normal form models. An …

Liu has written a comprehensive text on Web mining, which consists of two parts. The first
part covers the data mining and machine learning foundations, where all the essential …

Previous studies propose that associative classification has high classification accuracy and
strong flexibility at handling unstructured data. However, it still suffers from the huge set of …

Sophisticated online banking fraud reflects the integrative abuse of resources in social,
cyber and physical worlds. Its detection is a typical use case of the broad-based Wisdom …

We introduce a new kind of patterns, called emerging patterns (EPs), for knowledge
discovery from databases. EPs are defined as itemsets whose supports increase …

This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and
subgroup discovery (SD) in a unifying framework named supervised descriptive rule …

Having access to massive amounts of data does not necessarily imply that induction
algorithms must use them all. Samples often provide the same accuracy with far less …

The class imbalance problem is a challenge in supervised classification, since many
classifiers are sensitive to class distribution, biasing their prediction towards the majority …