|
Title:
|
MINING FREQUENT ITEMSETS WITH TS-TREE STRUCTURE USING CROSS VALIDATION APPROACH |
|
Author(s):
|
Savo Tomovi?, redrag Stanii? |
|
ISBN:
|
978-989-8533-06-7 |
|
Editors:
|
Hans Weghorn, Leonardo Azevedo and Pedro Isaías |
|
Year:
|
2011 |
|
Edition:
|
Single |
|
Keywords:
|
Frequent itemset mining, Apriori algorithm, cross validation |
|
Type:
|
Poster/Demonstration |
|
First Page:
|
593 |
|
Last Page:
|
596 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
We suggest a new method for frequent itemset mining which is based on cross validation method from artificial intelligence and machine learning. We partition the database into two subsets. First, we choose one of the subsets for training and the other for testing. From the training subset we mine frequent itemsets and use testing subset to calculate itemsets support in whole database. We then swap the roles of the subsets so that the previous training set becomes the test set and vice versa. Again we mine all frequent itemsets from training subset and use the other set to calculate supports in whole database. In this approach each record is used exactly once for training and once for testing which means that the database is read just twice. |
|
|
|
|
|
|
