A Modified Binary Gray Wolf Optimization for Feature Selection Using Elite Wolf in Unstructured Data Stream
Keywords:
cosine proximity, landmark window, session window, tumbling windowAbstract
Stream clustering poses challenges in feature selection due to data dynamics, variety, and a lack of labels in incoming data streams. While existing methods rely on labelled data, assuming structure in heterogeneous, unlabeled streams is unrealistic. To address this, we introduce a novel feature selection method, modified binary gray wolf optimization for stream feature selection (MBGWOSFS) using elite wolf, utilizing Evolutionary algorithm for unsupervised learning in streaming environments. Our novel feature selection method, aims to enhance clustering performance by selecting relevant features from unstructured data streams. Evaluation using internal metrics like Dunn Index, Davies-Bouldin Index, Calinski-Harabasz Index, and Silhouette Score, separation and compactness demonstrates that MBGWOSFS outperforms traditional methods by providing effective feature selection without relying on labelled data or predefined structures. With varying feature counts and high Dunn indices ranging from 57.846 to 72.7538, the method excels in cluster separation, reinforcing strong data similarity within clusters with Silhouette scores between 0.0324 and 0.047. Further, the well-balanced cluster quality, reflected in DB index and CH index values of 2.631 to 3.264 and 0.3688 to 0.43 respectively, showcases the adaptability and superior effectiveness of MBGWOSFS in text stream.
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