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.