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Extra resources for Advances in Database Technologies: ER ’98 Workshops on Data Warehousing and Data Mining, Mobile Data Access, and Collaborative Work Support and Spatio-Temporal Data Management, Singapore, November 19-20, 1998. Proceedings
Nishio, and T. Hasegawa, “Accuracy Evaluation of Rules Derived from Sample Data in VLKD,” Proc. 3-6, 1991. L. sg Abstract. , association rule discovery algorithms, functional dependency discovery algorithms, and correlation tests). Of these algorithms, only the correlation tests discover relationships using the measurement scales of attribute groups. Measurement scales determine whether order or distance information should be considered in the relationship discovery process. Order and distance information limits the possible forms a legitimate relationship between two attribute groups can have.
The possible representations of each data type are shown in Table 2. A set of heuristic rules are then applied to the attribute to identify which of the possible representations is the correct one. Many of these heuristic rules have been adapted from rules used in SNOUT  to identify measurement scales from survey data. Table 2. Initially Generated Hypotheses Type Integer Decimal String Date SAN SAD Ordinal Categorical Dichotomous √ √ √ √ √ √ √ √ √ √ √ √ √ The heuristic rules are listed below.
5% .. .. . . Fig. 4. Characteristic rules of the deduced relation. most can be generalized and this should be suﬃcient for our purpose of ﬁnding characteristic rules. If we randomly sample a certain number of tuples, for example two hundred tuples, from the deduced relation which contains attributes Patient and Ancestor and generalize the instances of Patient and Ancestor using the concept hierarchy in Fig. 3, we may obtain the characteristic rules of the deduced relation in the form shown in Fig.