Mining periodic-frequent itemsets with approximate periodicity using interval transaction-ids list tree

被引：22

作者：

Amphawan, Komate ^{[1
]}

Surarerks, Athatsit ^{[1
]}

Lenca, Philippe ^{[2
]}

机构：

[1] Chulalongkorn Univ, ELITE Lab, Bangkok, Thailand

[2] Univ Bretagne Occidentale, CNRS, Lab STICC, UMR 3192, Brest, France

来源：

THIRD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING: WKDD 2010, PROCEEDINGS | 2010年

关键词：

Data mining; knowledge discovery; frequent itemsets; periodic-frequent itemsets;

D O I：

10.1109/WKDD.2010.126

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Temporal periodicity of itemset appearance can be regarded as an important criterion for measuring the interestingness of itemsets in several application. A frequent itemset can be said periodic-frequent in a database if it appears at a regular interval given by the user. In this paper, we propose a concept of the approximate periodicity of each itemset. Moreover, a new tree-based data structure, called ITL-tree (Interval Transaction-ids List tree), is proposed. Our tree structure maintains an approximation of the occurrence information in a highly compact manner for the periodic-frequent itemsets mining. A pattern-growth mining is used to generate all of periodic-frequent itemsets by a bottom-up traversal of the ITL-tree for user-given periodicity and support thresholds. The performance study shows that our data structure is very efficient for mining periodic-frequent itemsets with approximate periodicity results.

引用

页码：245 / 248

页数：4