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Related Experiment Video

Updated: Dec 25, 2025

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
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Visual Structural Assessment and Anomaly Detection for High-Velocity Data Streams.

Punit Rathore, Dheeraj Kumar, James C Bezdek

    IEEE Transactions on Cybernetics
    |March 24, 2020
    PubMed
    Summary

    This study introduces an incremental algorithm (inc-siVAT) for analyzing high-velocity streaming data. It efficiently visualizes evolving cluster structures and detects anomalies in real-time data streams.

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    Area of Science:

    • Data Science
    • Machine Learning
    • Big Data Analytics

    Background:

    • Internet-of-Things (IoT), smartphones, and social media generate high-velocity data streams.
    • Timely interpretation of these streams is crucial for event detection, often appearing as clusters.
    • Existing Visual Assessment of Cluster Tendency (VAT) algorithms struggle with the volume and speed of streaming data.

    Purpose of the Study:

    • To propose an efficient incremental algorithm for analyzing evolving cluster structures in high-velocity data streams.
    • To address the limitations of existing VAT-based algorithms in terms of memory and processing speed.
    • To enable real-time anomaly detection and visualization of data stream evolution.

    Main Methods:

    • Developed an incremental scalable iVAT (inc-siVAT) algorithm for chunk-based stream processing.
    • Employed maximin random sampling (MMRS) for intelligent data sampling.
    • Utilized an incremental MMRS (inc-MMRS) to update samples on the fly.
    • Generated incrementally built iVAT images for visualization.

    Main Results:

    • The inc-siVAT algorithm effectively handles high-velocity and high-volume streaming data.
    • Demonstrated successful visualization of evolving cluster structures.
    • Showcased the ability to identify anomalies in dynamic datasets.
    • Validated through experiments on synthetic and real-world datasets.

    Conclusions:

    • The proposed inc-siVAT algorithm offers a scalable and efficient solution for analyzing streaming data.
    • It provides valuable insights into data stream evolution and supports anomaly detection.
    • This method is suitable for applications involving dynamic and large-scale data.