Discovery of Approximate Dependencies from Fuzzy Relational Databases

• Main Authors: Jenn-Shing Tsai, 蔡振興
• Other Authors: Shyue-Liang Wang
• Format: Others
• Language: en_US
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/70108554410275536947

碩士 === 義守大學 === 資訊工程學系 === 88 === We present here data mining techniques for discovering approximate dependencies based on equivalence classes from the similarity-based fuzzy relational database and fuzzy functional dependencies from the possibility-based fuzzy relational database. The similarity-based and possibility-based fuzzy data models are two major data models of fuzzy relational databases that have been proposed to represent imprecise, uncertain, and incomplete information. The similarity-based fuzzy data model extends the traditional relational model by allowing attribute values to be a subset of an attribute domain. In addition, similarity relation may exist between attribute values of a domain. The model has been recognized as most suitable for describing imprecise data that are analogical over discrete domains. The possibility-based fuzzy data model is another extension of traditional relational model in that attribute values may contain fuzzy sets. An approximate dependency can be considered as a functional dependency that almost holds. It describes approximate relationships between attributes of a relation in a database. Research on generalizing the notion of functional dependencies into that of approximate dependencies on fuzzy relational databases has been undertaken in recent years. Various forms of approximate dependencies have been proposed. However, their emphases are on the conceptual viewpoints and no mining algorithms are given. In this thesis, the problems of validity testings of approximate and fuzzy functional dependencies are studied. In addition, data mining techniques based on top-down levelwise searching are proposed here to discover for all possible minimal non-trivial approximate and fuzzy functional dependencies on similarity-based and possibility-based fuzzy relational databases respectively. Experimental results showing the behaviors of these approximate dependencies are discussed. The dependencies discovered contain not only the conventional functional dependencies when similarity relations are reduced to identity relations but also semantic dependencies that describe the conceptual structures between attributes. The results developed here can be applied to the areas of fuzzy database design, query optimization and database reverse engineering.