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@article{182811,
        author = {Snehal Kawale},
        title = {Machine Learning Techniques for Circular Data: A Novel Approach for Decision Trees and Clustering},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {3225-3227},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182811},
        abstract = {Data can be broadly categorized into linear and circular types, with circular data arising when measurements are directional or angular in nature, such as wind directions or time of day. Unlike linear data, circular data exhibits a cyclical structure—values wrap around at a boundary (e.g., 0° is equivalent to 360°)—posing unique challenges for traditional statistical and machine learning methods. Circular statistics, or directional statistics, specifically address these challenges by accounting for the inherent periodicity of such data. However, existing machine learning techniques have not been adequately adapted to effectively handle circular data. This research aims to bridge this gap by developing novel algorithms and methodologies tailored for circular data analysis. By integrating machine learning with circular statistical principles, our work seeks to enhance predictive modeling and insight extraction from directional datasets, with applications spanning meteorology, biology, and beyond.},
        keywords = {Circular Data,  Data Visualization, Decision Tree for Circular Data,  Machine Learning for Circular Data, Clustering Algorithms, Rainfall Prediction.},
        month = {July},
        }