Developing an efficient mining algorithm that can incrementally maintain discovered information as a database grows is quite important in the field of data mining. In the past, we proposed an incremental mining algorithm for maintenance of association rules as new transactions were inserted. Deletion of records in databases is, however, commonly seen in real-world applications. In this chapter, we first review the maintenance of association rules from data insertion and then attempt to extend it to solve the data deletion issue. The concept of pre-large itemsets is used to reduce the need for rescanning the original database and to save maintenance costs. A novel algorithm is proposed to maintain discovered association rules for deletion of records. The proposed algorithm doesn’t need to rescan the original database until a number of records have been deleted. If the database is large, then the number of deleted records allowed will be large too. Therefore, as the database grows, our proposed approach becomes increasingly efficient. This characteristic is especially useful for real-world applications.
A Review of Machine Learning Classification Using Quantum Annealing for Real-World Applications