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 …

Frequent pattern mining has been a focused theme in data mining research for over a
decade. Abundant literature has been dedicated to this research and tremendous progress …

Mining high utility itemsets from a transactional database refers to the discovery of itemsets
with high utility like profits. Although a number of relevant algorithms have been proposed in …

Recently, high utility pattern (HUP) mining is one of the most important research issues in
data mining due to its ability to consider the nonbinary frequency values of items in …

A graph-based approach to document classification is described in this paper. The graph
representation offers the advantage that it allows for a much more expressive document …

Mining High Average-Utility Itemsets (HAUIs) in a quantitative database is an extension of
the traditional problem of frequent itemset mining, having several practical applications …

Most algorithms for frequent pattern mining use a support constraint to prune the
combinatorial search space but support-based pruning is not enough. After mining datasets …

In this paper we propose a new and effective scheme for applying frequent itemset mining to
image classification tasks. We refer to the new set of obtained patterns as Frequent Local …

Mid-level or semi-local features learnt using class-level information are potentially more
distinctive than the traditional low-level local features constructed in a purely bottom-up …

High utility pattern (HUP) mining over data streams has become a challenging research
issue in data mining. When a data stream flows through, the old information may not be …