MAXLEN-FI: AN ALGORITHM FOR MINING MAXIMUM- LENGTH FREQUENT ITEMSETS FAST
Association rule mining, one of the most important and well-researched techniques of data mining. Mining frequent itemsets are one of the most fundamental and most time-consuming problems in association rule mining. However, real-world applications are often sufficient to mine a small representative...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Dalat University
2018-07-01
|
| Series: | Tạp chí Khoa học Đại học Đà Lạt |
| Subjects: | |
| Online Access: | http://tckh.dlu.edu.vn/index.php/tckhdhdl/article/view/407 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Association rule mining, one of the most important and well-researched techniques of data mining. Mining frequent itemsets are one of the most fundamental and most time-consuming problems in association rule mining. However, real-world applications are often sufficient to mine a small representative subset of frequent itemsets with low computational cost in generating association rules – maximum-length frequent itemsets. Maximum-length frequent itemsets can be useful in many application domains. In this paper, we proposed an algorithm called MAXLEN-FI for mining maximum-length frequent itemsets fast using an array of co-occurrence items. Finally, we presented experimental results on both synthetic and real-life datasets, which showed that the proposed algorithm performed better than the existing algorithms. |
|---|---|
| ISSN: | 0866-787X 0866-787X |