کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
384678 | 660853 | 2013 | 8 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Mining frequent itemsets in large databases: The hierarchical partitioning approach Mining frequent itemsets in large databases: The hierarchical partitioning approach](/preview/png/384678.png)
Although many methods have been proposed to enhance the efficiencies of data mining, little research has been devoted to the issue of scalability – that is, the problem of mining frequent itemsets when the size of the database is very large. This study proposes a methodology, hierarchical partitioning, for mining frequent itemsets in large databases, based on a novel data structure called the Frequent Pattern List (FPL). One of the major features of the FPL is its ability to partition the database, and thus transform the database into a set of sub-databases of manageable sizes. As a result, a divide-and-conquer approach can be developed to perform the desired data-mining tasks. Experimental results show that hierarchical partitioning is capable of mining frequent itemsets and frequent closed itemsets in very large databases.
► Mining frequent itemsets in large databases are a challenging task.
► A hierarchical partitioning approach is proposed for this task.
► The Frequent Pattern List can partition the databases hierarchically.
► Efficient algorithms can then be applied on smaller sub-databases.
► Experimental results verified the advantage of hierarchical partitioning.
Journal: Expert Systems with Applications - Volume 40, Issue 5, April 2013, Pages 1654–1661