Itemset share has been proposed as a measure of the importance of itemsets for mining association rules. The value of the itemset share can provide useful information such as total profit or total customer purchased quantity associated with an itemset in database. The discovery of share-frequent itemsets does not have the downward closure property. Existing algorithms for discovering share-freq...

The lift of an association rule is frequently used, both in itself and as a component in formulae, to gauge the interestingness of a rule. The range of values that lift may take is used to standarise lift so that it is more effective as a measure of interestingness. This standardisation is extended to account for minimum support and confidence thresholds. A method of visualising standardised li...

We focus on confidence-bounded association rules; we model a rather practical situation in which the confidence threshold is fixed by the user, as usually happens in applications. Within this model, we study notions of redundancy among association rules from a fundamental perspective: we discuss several existing alternative definitions and provide new characterizations and relationships between...

In data mining, the quality of an association rule can be stated by its support and its confidence. This paper investigates support and confidence measures for spatial and spatio-temporal data mining. Using fixed thresholds to determine howmany times a rule that uses proximity is satisfied seems too limited. It allows the traditional definitions of support and confidence, but does not allow to ...

In this paper, we propose an association-based video summarization scheme that mines sequential associations from video data for summary creation. Given detected shots of video V, we first cluster them into visually distinct groups, and then construct a sequential sequence by integrating the temporal order and cluster type of each shot. An association mining scheme is designed to mine sequentia...

In this paper, we study the issues of mining and maintaining association rules in a large database of customer transactions. The problem of mining association rules can be mapped into the problems of finding large itemsets which are sets of items bought together in a sufficient number of transactions. We revise a graph-based algorithm to further speed up the process of itemset generation. In ad...

We address the problem of the usefulness and the relevance of the set of discovered association rules. Using the frequent closed itemset groundwork, we propose to generate bases for association rules, that are non-redundant generating sets for all association rules.

Separate-and-conquer classifiers strongly depend on the criteria used to choose which rules will be included in the classification model. When association rules are employed to build such classifiers (as in ART [3]), rule evaluation can be performed attending to different criteria (other than the traditional confidence measure used in association rule mining). In this paper, we analyze the desi...

In this paper we describe an approach to classifying objects in a domain where classifications are uncertain using a novel combination of argumentation and data mining. Classification is the topic of a dialogue game between two agents, based on an argument scheme and critical questions designed for use by agents whose knowledge of the domain comes from data mining. Each agent has its own set of...

To My Parents, Family and Friends iv ACKNOWLEDGMENTS First and foremost, I would like to thank my advisor, Dr. Sharma Chakravarthy, for giving me an opportunity to work on this challenging topic and providing me ample guidance and support through the course of this research. Elkhalifa for their invaluable help and advice during the implementation of this work. I would like to thank all my frien...