Research On The Basic Technology Of Association Rules

Data mining means a process of finding nontrivial, extraction of implicit, pervious unknown and potential useful information from data in database. Association rule mining as an important field of data mining discovers interesting relationships among attributes in those data.By studying the literature domestic and abroad, we research some basic problems of association rules mining algorithms. The main contexts are showed as follows:Firstly, a maximal frequent itemset mining algorithm SFP-Miner, which based on Sorted FP-Tree was proposed. The SFP-Miner scanned Database twice and compress stored the frequent itemset in SFP-Tree. By using depth-first strategy, the algorithm pruned the searching space by pre-prune and mergence strategy and discovered all the maximal frequent itemset efficiently and didn't need to scan the Database. The experimental result indicated that SFP-Miner is an efficient algorithm.Secondly, we presented a new updating algorithm, UAMFI, for mining maximal frequent itemsets from transaction database when minimum support was changed by customer. The algorithm adopted a new data structure FMSFP-Tree (Full Merged SFP-Tree) which stored all the frequent itemsets in any given minimum support and it directly mined and updated the maximal frequent itemsets in FMSFP-Tree. It can efficiently mine maximal frequent itemsets with changed minimum support. From the experimental result, we can conclude that the algorithm is highly efficient to the updating mining problems.Finally, we presented a new algorithm, DBSMiner (Density Based Sub-space Miner), for mining quantitative attributes association rule. This algorithm, which referenced the ARCS (Association Rule Clustering System), used a grid structure to quantize the object space into a finite number of cells; it sorted all the dense grids by descending order and used a grid based cluster algorithm to cluster the data with all attributes. At last, it clustered the association rules. Theoretical analysis and experimental results show that, DBSMiner algorithm has good performance and accuracy. It can effectively mine association rule of quantitative attributes.