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• W1962592743 abstract "We propose to mine a set of rules from a collection of rule sets, each rule being discovered in a data set using a data mining algorithm. These meta-rules, rules about rules, represent the kind of knowledge that few existing data mining algorithms have been developed to mine for. In this study, we define problems in discovering the underlying regularities, differences, and changes hidden in rule sets and propose a new approach, meta-mining, which mines previous data mining results to discover these underlying regularities, differences, and changes. The purpose of meta-mining for regularities and for differences in rule sets is to discover association relationships. Meta-mining for regularities seeks to discover association relationships supported by a sufficiently large number of rules contained in just a few records in many data sets. Meta-mining for differences seeks to discover association relationships supported by a sufficiently small number of rules contained in many records in a small number of data sets. It would not be possible to distinguish between these two kinds of association relationships if the data sets were concatenated into a single data set. The associations that a large number of data sets have in common can be discovered in the form of rules. Their rule sets will contain a correspondingly large number of rules that support the associations. As these rules govern regular characteristics in the data sets, we refer to the rules for these rules as regular meta-rules. In contrast, the rules for some associations will be found in just a few data sets and their rule sets will contain a correspondingly smaller number of rules that support the associations. As these associations contribute to distinguishing or differentiating the data sets which contain them, we refer to the rules for these rules as differential meta-rules. Meta-mining can also be used to reveal changes in rule sets and this information can be used to discover change meta-rules, regularities governing how rules change over time. Change meta-rules can be used to predict how the rules will change in the future, freeing users from dependence on the historical data, allowing better planning, and making it possible to obviate or delay undesirable change. A meta-mining approach to the discovery of regular, differential, and change meta-rules should be able to (1) automatically generate fuzzy sets from data; (2) use linguistic variables and linguistic terms to represent regularities, differences, and changes; (3) exploit the scalability of parallel computer systems; (4) group and select a subset of attributes; and (5) enable the mining of association relationships involving attributes that were not originally contained in the data. (Abstract shortened by UMI.)" @default.
• W1962592743 created "2016-06-24" @default.
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• W1962592743 date "2006-01-01" @default.
• W1962592743 modified "2023-09-27" @default.
• W1962592743 title "A meta-mining approach to discovering regularities, differences, and changes in databases" @default.
• W1962592743 hasPublicationYear "2006" @default.
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