Associating absent frequent itemsets with infrequent items to identify abnormal transactions

被引：6

作者：

Kao, Li-Jen ^{[1
]}

Huang, Yo-Ping ^{[2
]}

Sandnes, Frode Eika ^{[3
,4
]}

机构：

[1] Hwa Hsia Univ Technol, Dept Comp Sci & Informat Engn, New Taipei City 23568, Taiwan

[2] Natl Taipei Univ Technol, Dept Elect Engn, Taipei 10608, Taiwan

[3] Fac Technol Art & Design, Inst Informat Technol, Oslo, Norway

[4] Akershus Univ, Coll Appl Sci, Oslo, Norway

来源：

APPLIED INTELLIGENCE | 2015年 / 42卷 / 04期

关键词：

Data mining; Abnormal transactions; Absent frequent itemset; Infrequent items; Association rules; HIGH-DIMENSIONAL DATA; OUTLIER DETECTION; DETECTION STRATEGY; ALGORITHMS;

D O I：

10.1007/s10489-014-0622-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Data stored in transactional databases are vulnerable to noise and outliers and are often discarded at the early stage of data mining. Abnormal transactions in the marketing transactional database are those transactions that should contain some items but do not. However, some abnormal transactions may provide valuable information in the knowledge mining process. The literature on how to efficiently identify abnormal transactions in the database as well as determine what causes the transactions to be abnormal is scarce. This paper proposes a framework to realize abnormal transactions as well as the items that induce the abnormal transactions. Results from one synthetic and two medical data sets are presented to compare with previous work to verify the effectiveness of the proposed framework.

引用

页码：694 / 706

页数：13

共 24 条

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Li-Jen Kao
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