Titlebook: Knowledge Discovery in Databases: PKDD 2003; 7th European Confere Nada Lavra?,Dragan Gamberger,Hendrik Blockeel Conference proceedings 2003

glomeruli

Majority Classification by Means of Association Rulesced amount of pruning, coupled with a two step classification process. . combines this approach with the use of multiple rules for data classification. The use of multiple rules, both during database coverage and classification, yields an improved accuracy.

hedonic

Minimal ,-Free Representations of Frequent Setsthe different proposals thus obtained, we are able to provide new, provably more concise, representations. These theoretical results are supported by several experiments showing the practical applicability.

Electrolysis

Discovering Unbounded Episodes in Sequential Dataepisodes to grow during the mining without any user-specified help. A convenient algorithm to efficiently discover the proposed unbounded episodes is also implemented. Experimental results confirm that our approach results useful and advantageous.

情節(jié)劇

monologue

A Skeleton-Based Approach to Learning Bayesian Networks from Datawork from the search space that is defined by the skeleton. To corroborate the feasibility of our approach, we present the experimental results that we obtained on various different datasets generated from real-world networks. Within the experimental setting, we further study the reduction of the search space that is achieved by the skeleton.

托運(yùn)

catagen

Exuberance

Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketinas. In the past few years, the number of published applications using propensity score methods to evaluate medical and epidemiological interventions has increased dramatically. Rubin (2003, Erlbaum) provides a summary, which is already out of date.

Foolproof

Forehead-Lift

ExAnte: Anticipated Data Reduction in Constrained Pattern Miningthm which reduces dramatically both the search space and the input dataset in constrained frequent pattern mining. Experimental results show a reduction of orders of magnitude, thus enabling a much easier mining task. ExAnte can be used as a pre-processor with any constrained pattern mining algorithm.