Info-fuzzy algorithms for mining dynamic data streams

Most data mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by dynamic processes, which may change over time, in some cases even drastically. The change in the underlying concept, also known as concept drift, causes the data mining model generated from past examples to become less accurate and relevant for classifying the current data. Most online learning algorithms deal with concept drift by generating a new model every time a concept drift is detected. On one hand, this solution ensures accurate and relevant models at all times, thus implying an increase in the classification accuracy. On the other hand, this approach suffers from a major drawback, which is the high computational cost of generating new models. The problem is getting worse when a concept drift is detected more frequently and, hence, a compromise in terms of computational effort and accuracy is needed....

• Type: Journal
• Year: 2008
• Where: ASC
• Authors: Lior Cohen, Gil Avrahami, Mark Last, Abraham Kandel
• Keywords: Algorithms, ASC 2006, ASC 2008, Concept Drift, Data Streams