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Associative classification is a rule-based approach to classify data relying on association rule mining by discovering associations between a set of features and a class label. Support and confidence are the de-facto interestingness measures used for discovering relevant association rules. The support-confidence framework has also been used in most, if not all, associative classifiers. Although support and confidence are appropriate measures for building a strong model in many cases, they are still not the ideal measures because in some cases a huge set of rules is generated which could hinder the effectiveness in some cases for which other measures could be better suited. There are many other rule interestingness measures already used in machine learning, data mining and statistics. This work focuses on using 53 different objective measures for associative classification rules. A wide range of UCI datasets are used to study the impact of different interestingness measures on different phases of associative classifiers based on the number of rules generated and the accuracy obtained. The results show that there are interestingness measures that can significantly reduce the number of rules for almost all datasets while the accuracy of the model is hardly jeopardized or even improved. However, no single measure can be introduced as an obvious winner.

Thesis (Ph. D.)--University of Oregon, 2006. / Typescript. Includes vita and abstract. Includes bibliographical references (leaf 77). Also available for download via the World Wide Web; free to University of Oregon users.

published_or_final_version / Mathematics / Doctoral / Doctor of Philosophy

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Contemporary theories of causal induction have focussed largely on the question of how evidence in the form of covariations between causes and effects is used to compute measures of causal strength. A very important precursor enabling such computations is that the reasoner notices that a cause and effect have co-occurred. Standard laboratory experiments have usually bypassed this problem by presenting participants directly with covariational information. As a result, relatively little is known about how humans identify causal relations in real time. What evidence exists, however, paints a rather unflattering picture of human causal induction and converges to the conclusion that humans cannot identify causal relations if cause and effect are separated by more than a few seconds. Associative learning theory has interpreted these findings to indicate that temporal contiguity is essential to causal inference. I argue instead that contiguity is not essential, but that the influence of time in causal inference is crucially dependent on people's beliefs and expectations about the timeframe of the causal relation in question. First I demonstrate that humans are capable of dissociating temporal contiguity from causal strength; more specifically, they can learn that a given event exerts a stronger causal influence when it is temporally separated from the effect than when it is contiguous with it. Then I re-investigate a paradigm commonly used to study the effects of delay on human causal induction. My experiments employed one crucial additional manipulation regarding participants' awareness of potential delays. This manipulation was sufficient to reduce the detrimental effects of delay. Three other experiments employed a similar strategy, but relied on implicit instructions about the timeframe of the causal relation in question. Overall, results support the hypothesis that knowledge mediates the timeframe of covariation assessment in human causal induction. Implications for associative learning and causal power theories are discussed.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1931. / Typescript. With this is bound: Linear associative algebras of infinite rank whose elements satisfy finite algebraic equations / by H.H. Conwell. Reprinted from Bulletin of the American Mathematical Society (Feb. 1934), p. 95-102. Includes bibliographical references.