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Mining Impact-Targeted Activity Patterns in Imbalanced Data.
- Source :
-
IEEE Transactions on Knowledge & Data Engineering . Aug2008, Vol. 20 Issue 8, p1053-1066. 14p. - Publication Year :
- 2008
-
Abstract
- Impact-targeted activities are rare but they may have a significant impact on the society. For example, isolated terrorism activities may lead to a disastrous event, threatening the national security. Similar issues can also be seen in many other areas. Therefore, it is important to identify such particular activities before they lead to having a significant impact to the world. However, it is challenging to mine impact-targeted activity patterns due to their imbalanced structure. This paper develops techniques for discovering such activity patterns. First, the complexities of mining imbalanced impact-targeted activities are analyzed. We then discuss strategies for constructing impact-targeted activity sequences. Algorithms are developed to mine frequent positive-impact-oriented (P → T¯) and negative-impact-oriented (P → T) activity patterns, sequential impact-contrasted activity patterns (P is frequently associated with both patterns P → T and P → T¯ in separated data sets), and sequential impact-reversed activity patterns (both P → T and PQ → T¯ are frequent). Activity impact modeling is also studied to quantify the pattern impact on business outcomes. Social security debt-related activity data is used to test the proposed approaches. The outcomes show that they are promising for information and security informatics (ISI) applications to identify impact-targeted activity patterns in imbalanced data. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 20
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 33379345
- Full Text :
- https://doi.org/10.1109/TKDE.2007.190635