Titlebook: Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques; Evangelos Triantaphyllou,Giovanni Felici Book 2006 Spri

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Deborah S. Keller,Scott R. Steele years have witnessed many efforts on discovering fuzzy associations, aimed at coping with fuzziness in knowledge representation and decision support processes. This chapter focuses on associations of three kinds: association rules, functional dependencies and pattern associations. Accordingly, it o

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Activity-Oriented Petri Nets (AOPN)word “sight” might become the word “site.” A spell checker cannot identify such an error. In the English language—the case of interest here—a syntax checker may also fail to catch such an error since, among other reasons, the parts-of-speech of an erroneous word may permit an acceptable parsing. Thi

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Run-Time Data Structures in GPenSIM. Fuzzy rules are extracted from the fuzzy clusters discovered by the fuzzy C-means clustering method. These rules can be used to characterize the semantical connections between keywords in a set of textual documents, and thus the rules can be used to improve the user queries for better retrieval pe

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Activity-Oriented Petri Nets (AOPN)sification rule as a decision to link or not to link two records in different databases in the absence of a common identifier. When a training data set of classified cases is available, developing a rule is easy; this chapter expands the application of the technique to situations where a training da

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A Common Logic Approach to Data Mining and Pattern Recognition,of Boolean or multi-valued attributes for modeling of the natural subject areas. Inductive inference used for extracting knowledge from data is combined with deductive inference, which solves other pattern recognition problems. A set of efficient algorithms was developed to solve the regarded proble

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The One Clause at a Time (OCAT) Approach to Data Mining and Knowledge Discovery,mathematical logic and discrete optimization. As input it uses samples of the performance of the system (or phenomenon) under consideration and then it extracts its underlying behavior in terms of a compact and rather accurate set of classification rules. This chapter also provides ways for decompos

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