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Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing
In this paper, we show that preprocessing data using a variant of rank transformation called 'Average Rank over an Ensemble of Sub-samples (ARES)' makes clustering algorithms robust to data representation and enable them to detect varying density clusters.
Advances and Challenges in Meta-Learning: A Technical Review
This review provides a comprehensive technical overview of meta-learning, emphasizing its importance in real-world applications where data may be scarce or expensive to obtain.
Improved histogram-based anomaly detector with the extended principal component features
In this era of big data, databases are growing rapidly in terms of the number of records.
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