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With advances in information and network technologies, lots of data have been digitized to reveal information for users by the construction of Web sites. Unfortunately, they are both overloading and overlapping in Internet so that users cannot distinguish their quality. To address this issue in education, Hwang, Huang, and Tseng proposed a group decision system to evaluate the quality of educational Web sites by users' and experts' opinions. Their investigative source is solely stemmed from human intention, called the "subjective perspective", to make judgments on the quality of Web sites. However, the nature of human beings in making decisions has a gap between intention and behavior. Asking people for eliciting thought is arduous to cause this gap. Human behavior, namely the objective perspective, is the other essential source to obtain human thinking and real doings. For this reason, we can use data mining approaches to acquire the objective source. In this research, we propose an integrated decision model applied in evaluating educational Web sites from the fuzzy subjective and "objective perspectives". The former source is extracted by inquiring human opinion using a questionnaire, while the latter is gained automatically by a data mining technique, fuzzy clustering. An empirical study is carried out to validate the model capability. (Contains 10 tables and 6 figures.)

Given a sequence database and minimum support threshold, the task of sequential pattern mining is to discover the complete set of sequential patterns in databases. From the discovered sequential patterns, we can know what items are frequently brought together and in what order they appear. However, they cannot tell us the time gaps between successive items in patterns. Accordingly, Chen et al. have proposed a generalization of sequential patterns, called time-interval sequential patterns, which reveals not only the order of items, but also the time intervals between successive items [9]. An example of time-interval sequential pattern has a form like (A, [I.sub.2], B, [I.sub.1], C), meaning that we buy A first, then after an interval of [I.sub.2] we buy B, and finally after an interval of [I.sub.1] we buy C, where [I.sub.2] and [I.sub.1] are predetermined time ranges. Although this new type of pattern can alleviate the above concern, it causes the sharp boundary problem. That is, when a time interval is near the boundary of two predetermined time ranges, we either ignore or overemphasize it. Therefore, this paper uses the concept of fuzzy sets to extend the original research so that fuzzy time-interval sequential patterns are discovered from databases. Two efficient algorithms, the fuzzy time interval (FTI)-Apriori algorithm and the FTI-PrefixSpan algorithm, are developed for mining fuzzy time-interval sequential patterns. In our simulation results, we find that the second algorithm outperforms the first one, not only in computing time but also in scalability with respect to various parameters. Index Terms--Data mining, fuzzy sets, sequence data, sequential patterns, time interval.