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A context‐driven intelligent database processing system using object‐oriented fuzzy cognitive maps
Author(s) -
Satur Richard,
Liu ZhiQiang
Publication year - 1996
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199609)11:9<671::aid-int6>3.0.co;2-h
Subject(s) - computer science , structuring , object (grammar) , context (archaeology) , fuzzy cognitive map , data mining , fuzzy set , artificial intelligence , fuzzy logic , set (abstract data type) , machine learning , fuzzy classification , paleontology , biology , finance , economics , programming language
Most data sets that describe and evolve from real‐world systems are by nature semiquantitative or qualitative rather than quantitative. This can mean large variations in the significance of results that are derived from this data for decision‐making processes given that the original database provides training and prototypical examples that reflect systems of events in the real world. In this article we propose a structure for a Knowledge‐Based System (KBS) that is derived using significance within given contextual domains. Data that would ordinarily be classified by simple attribute classification techniques are now categorized by understanding patterns and value distributions for attributes and attribute domains that exist within rich and dense databases such as in the case of census databases<‡> and Geographic Information Systems (GIS)<§> rich by the very number of fields and interpretations, depending on the context in which the data are to be reviewed. The structure we have implemented for capturing and structuring semiquantitative information is the Fuzzy Cognitive Map (FCM). We also reduce the number of false patterns labeled “significant” by incorporating the knowledge used by human experts to find significance within the data. We treat this knowledge as initial background knowledge and as a minimal set for distinguishing significance for particular attribute values within a given context. © 1996 John Wiley & Sons, Inc.