Incremental Rules Mining Algorithm Based on Incomplete Decision Table

Abstract:

The traditional approach to deal with incomplete information system is to make it completed, when a new object added only need a static attribute reduction algorithm to obtain the rules, wastes a lot of resources. The goal of incremental rules mining is to maintain the consistency of the rules in incomplete decision table. When a new object is added, establish discernibility matrix of the original decision table, get distribution reduction set, then generate conjunctive items export rules set. It introduces incremental learning concept, avoids tedious counting process. It can be effective for large-scale incomplete ocean data reduction and it also provides a strong basis for decision making for the marine environment processing and follow-up processing.

• Periodical: Applied Mechanics and Materials (Volumes 333-335)
• Pages: 1314-1318
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.333-335.1314
• Online since: July 2013
• Authors: De Xing Wang*, Hong Yan Lu, Hong Chun Yuan
• Keywords: Discernable Matrix, Incomplete Decision Table, Incremental Rule Mining, Resolution Function

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