Stochastic k-means for efficient higher order clustering
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Date
2023-07-03
Authors
Γεραμούτσου, Βασιλική
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Abstract
The advent of digital technologies has resulted in a wealth of data across various domains,
including the airline and music industries. This abundance of data allows for detailed insights into
consumer preferences, pop culture trends, and customer satisfaction with airline applications or
services. By systematically analyzing this data and leveraging machine learning models, valuable
insights can be derived. This work introduces a stochastic variant of the widely used k-means
clustering algorithm and provides guidelines for its implementation in Python. The stochastic kmeans
algorithm offers improved scalability and computational efficiency compared to its traditional
counterpart, making it suitable for large datasets and handling unknown attributes. Comprehensive
guidelines are presented for the Python implementation, covering essential steps such as
data preprocessing, distance calculation, centroid updating, and convergence criteria. These guidelines
serve as a valuable resource for future approaches, enabling the adoption and development
of stochastic clustering algorithms in data analysis. By following these guidelines, researchers and
practitioners can effectively apply the stochastic k-means algorithm and contribute to advancements
in the field. In conclusion, the combination of data analysis, machine learning, and the stochastic
k-means algorithm provides a powerful framework for gaining insights into customer satisfaction
and preferences. By leveraging these techniques, organizations in the airline industry can make
informed decisions to enhance their services and meet the evolving needs of their customers.
Description
Keywords
k-means, Classification, Stochastic clustering, Pythonic, Machine learning, Airlines, Spotify