Fast updating frequent itemset Talk to women sex site no sign up
The paper proposed a fast distributed mining algorithm of maximum frequent itemsets based on cloud computing, namely, FDMMFI algorithm.
FDMMFI algorithm made nodes compute local maximum frequent itemsets by cloud computing, then the center node exchanged data with other nodes and combined, finally, global maximum frequent itemsets were gained by cloud computing. Communications in Computer and Information Science, vol 391.
This site stores nothing other than an automatically generated session ID in the cookie; no other information is captured.
In general, only the information that you provide, or the choices you make while visiting a web site, can be stored in a cookie.
His research interests include association rules, classification, mining in incremental databases, distributed databases and privacy preserving in data mining.
Tuong Le received his BSc degree in Information Technology from University of Science, Vietnam National University, Ho Chi Minh City, Vietnam and MSc degree in Computer Science from University of Information Technology, Vietnam National University, Ho Chi Minh City, Vietnam in 20 respectively.
The FP-Growth Algorithm, proposed by Han in , is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree (FP-tree).
In his study, Han proved that his method outperforms other popular methods for mining frequent patterns, e.g. This chapter describes the algorithm and some variations and discuss features of the R language and strategies to implement the algorithm to be used in R.