Developing an efficient algorithm that can maintain discovered information as a database changes is quite important in data mining.Many proposed algorithms focused on a single level,and did not utilize previously mine...Developing an efficient algorithm that can maintain discovered information as a database changes is quite important in data mining.Many proposed algorithms focused on a single level,and did not utilize previously mined information in incrementally growing databases.In the past,we proposed an incremental mining algorithm for maintenance of multiple-level association rules as new transactions were inserted.Deletion of records in databases is,however,commonly seen in real-world applications.In this paper,we thus attempt to extend our previous approach to solve this issue.The concept of pre-large itemsets is used to reduce the need for rescanning original databases and to save maintenance costs.A pre-large itemset is not truly large,but promises to be large in the future.A lower support threshold and an upper support threshold are used to realize this concept.The two user-specified upper and lower support thresholds make the pre-large itemsets act as a gap to avoid small itemsets becoming large in the updated database when transactions are deleted.A new algorithm is thus proposed based on the concept to maintain discovered multiple-level 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.It can thus save much maintenance time.展开更多
文摘Developing an efficient algorithm that can maintain discovered information as a database changes is quite important in data mining.Many proposed algorithms focused on a single level,and did not utilize previously mined information in incrementally growing databases.In the past,we proposed an incremental mining algorithm for maintenance of multiple-level association rules as new transactions were inserted.Deletion of records in databases is,however,commonly seen in real-world applications.In this paper,we thus attempt to extend our previous approach to solve this issue.The concept of pre-large itemsets is used to reduce the need for rescanning original databases and to save maintenance costs.A pre-large itemset is not truly large,but promises to be large in the future.A lower support threshold and an upper support threshold are used to realize this concept.The two user-specified upper and lower support thresholds make the pre-large itemsets act as a gap to avoid small itemsets becoming large in the updated database when transactions are deleted.A new algorithm is thus proposed based on the concept to maintain discovered multiple-level 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.It can thus save much maintenance time.
