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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
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SOMKE: kernel density estimation over data streams by sequences of self-organizing maps.

Yuan Cao, Haibo He, Hong Man

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2014
    PubMed
    Summary

    We introduce SOMKE, a novel method for kernel density estimation (KDE) on data streams using self-organizing maps (SOMs). SOMKE efficiently estimates probability distributions in data streams, outperforming traditional methods in accuracy and speed.

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    Area of Science:

    • Data Mining
    • Machine Learning
    • Statistical Modeling

    Background:

    • Traditional kernel density estimation (KDE) methods face challenges with high computational cost, processing time, and memory requirements in data stream mining applications.
    • Efficiently estimating probability distributions from continuous data streams is crucial for various data mining tasks.

    Purpose of the Study:

    • To propose a novel and efficient method, SOMKE, for kernel density estimation (KDE) over data streams.
    • To reduce the time and space complexity associated with traditional KDE methods for streaming data.
    • To accurately estimate probability density functions for both stationary and nonstationary data streams.

    Main Methods:

    • SOMKE utilizes sequences of self-organizing maps (SOMs) to cluster incoming data streams.
    • The method involves creating SOM sequence entries for data windows and merging consecutive entries using Kullback-Leibler divergence to reduce sequence size.
    • Probability density functions are estimated using these optimized SOM sequences.

    Main Results:

    • SOMKE demonstrates superior accuracy and processing time compared to M-kernel and cluster kernel approaches on stationary data streams.
    • The method's effectiveness is validated on nonstationary data streams, including synthetic, financial, and network traffic data.
    • Simulation results confirm the efficiency and effectiveness of the proposed SOMKE approach.

    Conclusions:

    • SOMKE offers an efficient and accurate solution for kernel density estimation in data stream mining.
    • The use of self-organizing map sequences effectively addresses the limitations of traditional KDE methods for high-velocity data.
    • The proposed approach is suitable for both stationary and evolving data stream environments.