Topic 6- Dq-2-4
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Grand Canyon University *
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Industrial Engineering
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Dec 6, 2023
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docx
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Uploaded by ConstableLion3405
Using a hybrid approach, combining supervised and unsupervised methods is an efficient means
of data mining due to several reasons. Supervised methods rely on a labeled training set to learn
the model parameters and make predictions. However, labeled data may only sometimes be
available. Combining unsupervised methods with supervised methods allows for the utilization
of unlabeled data, which is often more abundant and readily available (Li et al., 2018). The
hybrid approach can handle complex and high-dimensional data more effectively by capturing
both the global structure and local patterns present in the data.
Unsupervised methods can be used to preprocess the data and identify hidden patterns or
structures within the dataset. This preprocessing step can help in feature extraction,
dimensionality reduction, and clustering, which can be used as input for the supervised learning
algorithm (Jayatilake & Ganegoda, 2021). Unsupervised methods can be used to reduce the
dimensionality or complexity of the data, reducing overfitting in supervised models and leading
to more robust and accurate predictions (Maturo & Verde, 2022). This allows for a more
comprehensive understanding of the data and results in more accurate predictions.
Combining unsupervised with supervised methods can improve accuracy, feature engineering,
and more robust model training. However, determining if a hybrid approach is appropriate
requires a clear understanding of the problem-specific goals of the data mining task.
References
Jayatilake, S. M. D. A. C., & Ganegoda, G. U. (2021). Involvement of Machine Learning Tools
in Healthcare Decision Making.
Journal of Healthcare Engineering, 2021
, 1–
20.
https://doi.org/10.1155/2021/6679512
Li, N., Martin, A., & Estival, R. (2018). Combination of supervised learning and unsupervised
learning based on object association for land cover classification.
2018 Digital Image
Computing: Techniques and Applications (DICTA)
.
https://doi.org/10.1109/dicta.2018.8615871
Maturo, F., & Verde, R. (2022). Combining unsupervised and supervised learning techniques for
enhancing the performance of functional data classifiers. Computational
Statistics.
https://doi.org/10.1007/s00180-022-01259-8
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