The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Feature Selection for Clustering: A Review
Abstract
Dimensionality reduction techniques can be categorized mainly into feature extraction and feature selection. In the feature extraction approach, features are projected into a new space with lower dimensionality. Feature selection is broadly categorized into four models: filter model, wrapper model, embedded model, and hybrid model. With the existence of a large number of features, learning models tend to overfit and their learning performance degenerates. Feature selection is one of the most used techniques to reduce dimensionality among practitioners. The existence of irrelevant features in the data set may degrade learning quality and consume more memory and computational time that could be saved if these features were removed. However, finding clusters in high-dimensional space is computationally expensive and may degrade the learning performance. Clustering is useful in several machine learning and data mining tasks including image segmentation, information retrieval, pattern recognition, pattern classification, and network analysis.
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Cites
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Unsupervised deep embedding for clustering analysis
Xie, J., Girshick, R., & Farhadi, A. (2015) · arXiv · doi:10.48550/arxiv.1511.06335 · Cited by 2,476
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection
Ang, J. C., Mirzal, A., Haron, H., & Hamed, H. N. A. (2016) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2478454 · Cited by 641
A Study on Human Activity Recognition Using Accelerometer Data from Smartphones
Bayat, A., Pomplun, M., & Tran, D. A. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.07.009 · Cited by 531
Cluster analysis of acoustic emission signals for 2D and 3D woven glass/epoxy composites
Li, L., Lomov, S. V., Yan, X., & Carvelli, V. (2014) · Composite Structures · doi:10.1016/j.compstruct.2014.05.023 · Cited by 255
Statistical analysis of proteomics data: A review on feature selection
Lualdi, M., & Fasano, M. (2019) · Journal of Proteomics · doi:10.1016/j.jprot.2018.12.004 · Cited by 93
Metadata
- Authors
- Alelyani, S., Tang, J., & Liu, H.
- Venue
- Data Clustering
- Publisher
- Chapman and Hall/CRC
- Published
- 2018-09-03
- Pages
- 29-60
- OpenAlex
- W2398336627
- Cited by
- 371
- References
- 68
- FWCI
- 63.21
- Type
- book chapter
- Language
- en
- Open access
- closed
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