During decision making, we are often confronted by a huge amount of factors. These factors
may be either an advantage or a disadvantage to a decision objective. For the purpose of …

This paper presents the APRIORI-C algorithm, modifying the association rule learner
APRIORI to learn classification rules. The algorithm achieves decreased time and space …

Inductive databases tightly integrate databases with data mining. The key ideas are that data
and patterns (or models) are handled in the same way and that an inductive query language …

Frequent sets lie at the basis of many Data Mining algorithms. As a result, hundreds of
algorithms have been proposed in order to solve the frequent set mining problem. In this …

We introduce the Boolean inductive query evaluation problem, which is concerned with
answering inductive queries that are arbitrary Boolean expressions over monotonic and anti …

In data mining applications, it is important to develop evaluation methods for selecting
quality and profitable rules. This paper utilizes a non-parametric approach, Data …

Nowadays, with the rise in sensor technology, the amount of spatial and temporal data
increases day by day. Fast, effective, and accurate analysis and prediction of collected data …

Progress in digital data acquisition, distribution, retrieval and storage technology has
resulted in the growth of massive databases. One of the greatest challenges facing …

On account of the enormous amounts of rules that can be produced by data mining
algorithms, knowledge post-processing is a difficult stage in an association rule discovery …

In the context of mining for frequent patterns using the standard level-wise algorithm, the
following question arises: given the current level and the current set of frequent patterns …