Title :
The Research of FP-Growth Method Based on Apriori Algorithm in MDSS
Author :
Min, Li ; Chunyan, Wang ; Yuguang, Yan
Author_Institution :
Coll. of Comput., Changchun Normal Univ., Changchun, China
Abstract :
The paper mainly discussed The Application of the Model_FP based on Apriori algorithm in MDSS. FP-Growth algorithm based on frequent pattern growth (frequent-pattern growth referred to as FP-growth) model - model Model_FP, it has taken the following sub-rule strategy: frequent item sets will be provided to a frequent pattern database compression tree (or FP-tree), but remains set of related information items, then, will take this compressed database into a set of the database (a special type of projection database), each associated with a frequent item, and were excavated each database. FP-growth method will change find long frequent patterns to find the problem into a number of short-recursive mode, then connect the suffix. It uses the least frequent items as suffix, to provide a good selectivity, and the method reduces the search overhead.
Keywords :
data compression; data warehouses; medical computing; trees (mathematics); FP-growth method; MDSS; apriori algorithm; data warehouse; frequent item sets; frequent pattern database compression tree; least frequent items; medical decision supporting system; Apriori algorithm; FP-growth method; Model_FP model; Supporting System of Medical Decision;
Conference_Titel :
Digital Manufacturing and Automation (ICDMA), 2010 International Conference on
Conference_Location :
ChangSha
Print_ISBN :
978-0-7695-4286-7
DOI :
10.1109/ICDMA.2010.169