Abstract
Incremental data mining is very important to solve the temporal dynamic property of knowledge, improve the performance of mining processes and efficiency of mining results. Incremental data occurs with the passage of time. Evolutionary methods can be adopted to solve such increment. A multiply evolutionary model is built to describe incremental data evolutionary mining processes. Copy operator, cross operator and mutation operator are designed. A general algorithm for dynamic evolutionary mining is also presented. The experiments showed that the evolutionary incremental data mining method could solve the scalable problem of data mining better, and have high accuracy and good time performance.
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