A FUZZY MINING APPROACH FOR AN ENCODED TEMPORAL DATABASE WITH LOWER COMPLEXITIES OF TIME AND COMPUTATION
Databases and data warehouses have become a vital part of many organizations. So useful information and helpful knowledge have to be mined from transactions. The principle of data mining is better to use complicative primitive patterns and simple logical combination than simple primitive patterns and complex logical form. This paper overviews the concept of temporal database encoding, association rules mining. It proposes an innovative approach of data mining to reduce the size of the main database by an encoding method which in turn reduces the memory required. The use of the anti-Apriori algorithm reduces the number of scans over the database. A graph based approach for identifying frequent large item sets involves less time complexity. The fuzzy approach that integrates fuzzy-set concepts with Apriori when used for temporal mining involves less computational complexity. Experimental study has proved that the fuzzy approach performs better by resulting in lesser time and computational complexity then the other approaches for rule mining on an encoded temporal database.
Anti-apriori algorithm Association rules mining Data mining Fuzzy approach Graph based approach Temporal database encoding
C.Balasubramanian K.Duraiswamy
Department of Computer Science and Engineering K.S.Rangasamy College of Technology Namakkal-Dt., Tamilnadu, India
国际会议
北京
英文
2259-2263
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）