Abstract:

The field of data mining association rules is one of the most important and active areas .Taking the Apriori algorithm as a premise , using the Affairs compression idea of AprioriTid algorithms, we reduce the duplication of time scanning the database. We put forward a kind of Apriori algorithm  based on the identifier lists of transactions in the database, and the list length is the candidate sets’ corresponding support count. For getting the support count in the calculation, we only need to count the length of the list, thereby reducing the calculation time. At the same time, introducing the address indexing mechanism when generating frequent itemsets in the pruning process, we use the first set of candidate elements in the address table index to quickly locate, and thus reduce the number of scanning the transaction database. We make use of the business address index table to improve the counting time and execution efficiency of algorithms.The data of scientific research management as the research object, we use the improved algorithms to analyze the data of relationship, moreover, to extract the data’s hidden ,valuable information, and support the next phase of scientific research management. The experiments show that the algorithm is more efficient.

Key words: association rule;data mining;apriori algorithm;allocation index