An Incremental Interesting Maximal Frequent Itemset Mining Based on FP-Growth Algorithm
Frequent itemset mining is the most important step of association rule mining. It plays a very important role in incremental data environments. The massive volume of data creates an imminent need to design incremental algorithms for the maximal frequent itemset mining in order to handle incremental...
Saved in:
Main Authors: | Hussein A. Alsaeedi, Ahmed S. Alhegami |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2022/1942517 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering
by: M. Sinthuja, et al.
Published: (2025-03-01) -
MAXLEN-FI: AN ALGORITHM FOR MINING MAXIMUM- LENGTH FREQUENT ITEMSETS FAST
by: Phan Thành Huấn, et al.
Published: (2018-07-01) -
DISCOVERING CONFUSING FREQUENT ITEMSETS
by: Huỳnh Thành Lộc
Published: (2018-07-01) -
Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response
by: Chongjing Sun, et al.
Published: (2014-01-01) -
Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data
by: András Király, et al.
Published: (2014-01-01)