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Mining impact-targeted activity patterns in imbalanced data
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DOI：10.1109/TKDE.2007.190635.png)
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摘要
En 中文
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) over bar) activity patterns, sequential impact-contrasted activity patterns (P is frequently associated with both patterns P -> T and P -> (T) over bar in separated data sets), and sequential impact-reversed activity patterns (both P -> T and PQ -> (T) over bar 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.
Keyword:
clustering
classification
association rules
data mining
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10.4
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