Association rules are commonly used to provide decision‐makers with knowledge that helps
them to make good decisions. Most of the published proposals mine association rules …

Temporal data contain time-stamping information that affects the results of data mining.
Traditional techniques for finding frequent itemsets assume that datasets are static and the …

The problem of mining frequent patterns in non-temporal databases is studied extensively.
Conventional frequent pattern algorithms are not applicable to find temporal frequent items …

Temporal database is a database which captures and maintains past, present and future
data. Conventional databases are not suitable for handling such time varying data. In this …

Recently more encroachment has emerged in the field of data mining. One of the hottest
topic in this area is mining for hidden patterns from the existing massive collection of …

Much of the work in the data mining community mines temporal knowledge based primarily
on the frequency of events, eg, frequent pattern mining, ignoring their duration. This paper …

This article deals with data mining applications for the supply chain inventory management.
ABC classification is usually used for inventory items classification because the number of …

It is widely considered that approximately 10% of the population suffers from type 2 diabetes.
Unfortunately, the impact of this disease is underestimated. Patient's mortality often occurs …

A novel framework for mining temporal association rules by discovering itemsets with a
genetic algorithm is introduced. Metaheuristics have been applied to association rule …

Temporal association rule mining is a data mining technique in which relationships between
items which satisfy certain timing constraints can be discovered. This paper presents the …