A framework for estimating complex probability density structures in data streams

  title={A framework for estimating complex probability density structures in data streams},
  author={Arnold P. Boedihardjo and Chang-Tien Lu and Feng Chen},
Probability density function estimation is a fundamental component in several stream mining tasks such as outlier detection and classification. The nonparametric adaptive kernel density estimate (AKDE) provides a robust and asymptotically consistent estimate for an arbitrary distribution. However, its extensive computational requirements make it difficult to apply this technique to the stream environment. This paper tackles the issue of developing efficient and asymptotically consistent AKDE… CONTINUE READING


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