Using the structure of prelarge trees to incrementally mine frequent itemsets

Chun Wei Lin, Tzung Pei Hong, Wen-Hsiang Lu

研究成果: Article同行評審

10 引文 斯高帕斯(Scopus)

摘要

The frequent pattern tree (FP-tree. is an efficient data structure for association-rule mining without generation of candidate itemsets. It was used to compress a database into a tree structure which stored only large items. It, however, needed to process all transactions in a batch way. In the past, we proposed a Fast Updated FP-tree (FUFP-tree. structure to efficiently handle new transactions and to make the tree update process become easier. In this paper, we propose the structure of prelarge trees to incrementally mine association rules based on the concept of pre-large itemsets. Due to the properties of pre-large concepts, the proposed approach does not need to rescan the original database until a number of new transactions have been inserted. The proposed approach can thus achieve a good execution time for tree construction especially when a small number of transactions are inserted each time. Experimental results also show that the proposed approach has a good performance for incrementally handling new transactions.

原文English
頁(從 - 到)5-20
頁數16
期刊New Generation Computing
28
發行號1
DOIs
出版狀態Published - 2010 1月 1

All Science Journal Classification (ASJC) codes

  • 軟體
  • 理論電腦科學
  • 硬體和架構
  • 電腦網路與通信

指紋

深入研究「Using the structure of prelarge trees to incrementally mine frequent itemsets」主題。共同形成了獨特的指紋。

引用此