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Research On Algorithm Of Mining Association Rules Based On Fp Tree

Posted on:2011-01-05Degree:MasterType:Thesis
Country:ChinaCandidate:X WeiFull Text:PDF
GTID:2198330332488166Subject:Computer software and theory
Abstract/Summary:PDF Full Text Request
Being an extremely essential research topic in data mining,association rules mining is widely applied in various fields. Association rules may both examine the knowledge pattern formed for a long time in the profession and discover the secret new rules. The discovery, comprehension and application of association rules are important means of accomplishing the task of data mining. Therefore, the research of association rules mining is of great importance in both theoretical realm and realistic realm.The thesis analyses the disadvantage of FP-Growth in depth. Taking measures from data structure and mining means, a novel algorithm for mining frequent patterns based on improved compressed FP tree is proposed. This algorithm saves large memory space occupied by FP tree and the cost of constructing many conditional FP trees. Experiments show that the time and space for the improved algorithm have reduced significantly compared to FP-Growth mining.Then, Increase in the case of database records, a Maximum frequent item-sets of the most efficient incremental update problem. In processing new work.this algorithm no longer adds new nodes to the FP tree or support count of any node.Instead it creates new sub tree of root or adds nodes to the new sub tree or adds support count of any node. This algorithm only handles newly increased frequent items instead of frequent items whose support count dose not change. The experiment result shows that this algorithm is more efficient than the traditional algorithm based on FP tree for mining maximum frequent item-sets.
Keywords/Search Tags:Association Rules, FP tree, Frequent Pattern, Maximum frequent item-sets, Incremental Updating
PDF Full Text Request
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