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One of the greatest challenges of data mining is dealing with very large datasets. Cloud computing has demonstrated great advantages in processing very large datasets. When considering taking advantage of the high performance data cloud to do data mining, there are different approaches to make an existing data mining algorithm parallelizable in a cloud computing environment. One concern is how to...
The application of frequent patterns in classification has demonstrated its power in recent studies. It often adopts a two-step approach: frequent pattern (or classification rule) mining followed by feature selection (or rule ranking). However, this two-step process could be computationally expensive, especially when the problem scale is large or the minimum support is low. It was observed that frequent...
Many applications are driven by evolving data - patterns in Web traffic, program execution traces, network event logs, etc., are often non-stationary. Building prediction models for evolving data becomes an important and challenging task. Currently, most approaches work by "chasing trends", that is, they keep learning or updating models from the evolving data, and use these impromptu models...
Classification is an important data analysis tool that uses a model built from historical data to predict class labels for new observations. More and more applications are featuring data streams, rather than finite stored data sets, which are a challenge for traditional classification algorithms. Concept drifts and skewed distributions, two common properties of data stream applications, make the task...
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