TY - JOUR
T1 - FP-growth Tree for large and Dynamic Data Set and Improve Efficiency
AU - Rahul Moriwal
JO - Journal of Information and Computing Science
VL - 2
SP - 083
EP - 090
PY - 2024
DA - 2024/01
SN - 9
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22584.html
KW - Divide & Conquer, partitioning-based, parallel projection, data mining, AI  

AB - FP-growth method is an efficient algorithm to mine  frequent patterns, in spite of long or short 
frequent  patterns.  By  using  compact  tree  structure  and  partitioning-based,  divide-and-conquer  searching 
method, it reduces the search costs substantially. But just as the analysis in Algorithm, in the process of FP-
tree construction, it is a strict serial computing process. Algorithm performance is related to the database size, 
the  sum  of  frequent  patterns  in  the  database:  ω.  this  is  a  serious  bottleneck.  People  may  think  using 
distributed parallel computation technique or multi-CPU to solve this problem. But these methods apparently 
increase the costs for exchanging and combining control information, and the algorithm complexity is also 
greatly  increased,  cannot  solve  this  problem  efficiently.  Even  if  adopting  multi-CPU  technique,  raising  the 
requirement of hardware, the performance improvement is still limited.