The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Data mining in soft computing framework: a survey
Abstract
The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.
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Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
The KDD process for extracting useful knowledge from volumes of data
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996) · Communications of the ACM · doi:10.1145/240455.240464 · Cited by 1,934
Taking on the curse of dimensionality in joint distributions using neural networks
Bengio, S., & Bengio, Y. (2000) · IEEE Transactions on Neural Networks · doi:10.1109/72.846725 · Cited by 115
Cited by
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.08.006 · Cited by 1,361
Tsai, C.-W., Lai, C.-F., Chao, H.-C., & Vasilakos, A. V. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0030-3 · Cited by 818
Data mining in manufacturing: a review based on the kind of knowledge
Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2009) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-008-0145-x · Cited by 494
A survey of Knowledge Discovery and Data Mining process models
KURGAN, L. A., & MUSILEK, P. (2006) · The Knowledge Engineering Review · doi:10.1017/s0269888906000737 · Cited by 391
Multi-Modal Stacked Denoising Autoencoder for Handling Missing Data in Healthcare Big Data
Kim, J.-C., & Chung, K. (2020) · IEEE Access · doi:10.1109/access.2020.2997255 · Cited by 56
Metadata
- Authors
- Mitra, S., Pal, S. K., & Mitra, P.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2002
- Volume
- 13
- Issue
- 1
- Pages
- 3-14
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2143533311
- Cited by
- 617
- References
- 4
- FWCI
- 27.43
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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