A Constrained Maximum Frequent Itemsets Incremental Mining Algorithm
NPC '07 Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops
Top 10 algorithms in data mining
Knowledge and Information Systems
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PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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According to the stored mode of massive data in the relational database, this paper proposed a fast mining algorithm to find maximum frequent item sets based on item sequence set grid space. The traditional methods for mining association rules generate frequent item sets from small to large. These approaches are either time consuming or computationally expensive, and often generate a large number of redundant candidates or frequent item sets, which is fatal for controlling mining speed as data to mass-level. The goal of this paper is first to use a self-defined structure linked list to storage item sequence then to find the frequent item sets from large to small. Several applications of association rules mining using item sequence set grid space has a good performance but it demonstrated inefficiency in massive data mining. The problem involves time spent on sub item sets finding. Experimental results will be presented to show that the fast mining algorithm ISSDL-DM proposed in this paper use much less time than the similar existing algorithm ISS-DM for achieving the same outcomes.