Typically association rule mining only considers positive frequent itemsets in rule generation, where rules involving only the presence of items are generated. In this paper we consider the complementary problem of negative association rule mining, which generates rules describing the absence of itemsets from transactions. We describe a new approach called MINR (Mining Interesting Negative Rule...

In this paper 1 we are concerned in looking at different ways for calculating the strength of Association Rules in Market Basket data. The significance of Association rules is measured via support and confidence and the way they are used to identify the rules in a particular transaction of the form, “When a customer buys items A&B also buys item C”. The first part of this paper illustrates the ...

Narrowing down the computational space is a key factor in improving the efficiency of an association rule mining system. One approach to achieve this is to let the user guide the association rule mining process by enabling the user to specify the types of association rules that he/she might be interested in. Instead of computing all that can be computed, the system limits its association rule m...

Tweets are short messages that do not exceed 140 characters. Since they must be written respecting this limitation, a particular vocabulary is used. To make them understandable to a reader, it is therefore necessary to know their context. In this paper, we describe our approach submitted for the tweet contextualization track in CLEF 2014 (Conference and Labs of Evaluation Forums). This approach...

Association rule mining is well-known to depend heavily on a support threshold parameter, and on one or more thresholds for intensity of implication; among these measures, confidence is most often used and, sometimes, related alternatives such as lift, leverage, improvement, or all-confidence are employed, either separately or jointly with confidence. We remain within the support-and-confidence...

The paper presents the problem of the unsupervised discretization of continuous attributes for association rules mining. It shows commonly used techniques for this aim and highlights their principal limitations. To overcome such limitations a method based on the use of a SOM is presented and tested over various real world datasets.