In this paper, we proposed not only an extraction methodology of multiple feature vectors from ultrasound images for carotid arteries (CAs) and heart rate variability (HRV) of electrocardiogram signal, but also a suitable and reliable prediction model useful in the diagnosis of cardiovascular disease (CVD). For inventing the multiple feature vectors, we extract a candidate feature vector through image processing and measurement of the thickness of carotid intima-media (IMT). As a complementary way, the linear and/or nonlinear feature vectors are also extracted from HRV, a main index for cardiac disorder. The significance of the multiple feature vectors is tested with several machine learning methods, namely Neural Networks, Support Vector Machine (SVM), Classification based on Multiple Association Rule (CMAR), Decision tree induction and Bayesian classifier. As a result, multiple feature vectors extracted from both CAs and HRV (CA+HRV) showed higher accuracy than the separative feature vectors of CAs and HRV. Furthermore, the SVM and CMAR showed about 89.51% and 89.46%, respectively, in terms of diagnosing accuracy rate after evaluating the diagnosis or prediction methods using the finally chosen multiple feature vectors. Therefore, the multiple feature vectors devised in this paper can be effective diagnostic indicators of CVD. In addition, the feature vector analysis and prediction techniques are expected to be helpful tools in the decisions of cardiologists.

Web log mining is an important area of Web Usage Mining (WUM) for discovering useful information from web log files produced by web servers. According to the mining task, user access patterns can be extracted and the result can be applied to improve websites, business intelligence systems and other areas. In this paper, we propose a novel approach for exacting user access patterns by achieving a large event set and improving the BI-Directional Extension closure checking method (BIDE). The process of getting a large event set can filter out the more frequent events and discard the infrequent events to reduce the number of data. Compared with other methods, the BIDE method with the constraint of gap can achieve a more efficient mining process and generate more accurate results. In our experiment, the effect of the mining task is discussed and the applications of this approach are presented.