Under frameworks such as Alcohol Myopia Theory, a body of literature has developed demonstrating how alcohol intoxication can increase behavioral risk-taking, potentially via impaired inhibition of prepotent behavioral responses. A separate area of research has shown that responses to alcohol intoxication are not homogenous across the population. Whereas most previous research has considered alcohol responses in relation to risk for alcohol use disorders, the present investigation tested whether they may additionally contribute to the acute effects of alcohol on drinking-episode-specific cognitive and behavioral consequences. We recruited 82 moderate-to-heavy drinking emerging adults to each complete 2 research protocols: a placebo-controlled, within-subject, counterbalanced alcohol challenge in a simulated bar laboratory and a 21-day, event-level self-monitoring follow-up. Replicating previous research, the alcohol challenge increased heart rate and subjective stimulant-like and sedative-like responses and impaired psychomotor performance and response inhibition. Individual differences in subjective stimulation but not sedation were significantly associated with inhibitory impairment. In the event-level follow-up, we found little evidence that alcohol responses elevated risk for adverse behavioral outcomes, although evidence was stronger that alcohol responses were associated with alcohol-induced memory blackout. Whether and how alcohol responses relate to the physiological, cognitive, and behavioral consequences of alcohol intoxication may depend on a) the quality of the response (e.g., stimulation vs. sedation), b) the type of outcome (e.g., response inhibition vs. blackout vs. behavioral risk-taking), and c) whether perceptions of alcohol-induced effects may contribute to emerging adults' evaluations of risk (e.g., driving after drinking and riding with a drinking driver). / text

For a large mixed-mode database, how to discretize its continuous data into interval events is still a practical approach. If there are no class labels for the database, we have nohelpful correlation references to such task Actually a large relational database may contain various correlated attribute clusters. To handle these kinds of problems, we first have to partition the databases into sub-groups of attributes containing some sort of correlated relationship. This process has become known as attribute clustering, and it is an important way to reduce our search in looking for or discovering patterns Furthermore, once correlated attribute groups are obtained, from each of them, we could find the most representative attribute with the strongest interdependence with all other attributes in that cluster, and use it as a candidate like a a class label of that group. That will set up a correlation attribute to drive the discretization of the other continuous data in each attribute cluster. This thesis provides the theoretical framework, the methodology and the computational system to achieve that goal.

For a large mixed-mode database, how to discretize its continuous data into interval events is still a practical approach. If there are no class labels for the database, we have nohelpful correlation references to such task Actually a large relational database may contain various correlated attribute clusters. To handle these kinds of problems, we first have to partition the databases into sub-groups of attributes containing some sort of correlated relationship. This process has become known as attribute clustering, and it is an important way to reduce our search in looking for or discovering patterns Furthermore, once correlated attribute groups are obtained, from each of them, we could find the most representative attribute with the strongest interdependence with all other attributes in that cluster, and use it as a candidate like a a class label of that group. That will set up a correlation attribute to drive the discretization of the other continuous data in each attribute cluster. This thesis provides the theoretical framework, the methodology and the computational system to achieve that goal.

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