The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Data mining and knowledge discovery: making sense out of data
Abstract
Current computing and storage technology is rapidly outstripping society's ability to make meaningful use of the torrent of available data. Without a concerted effort to develop knowledge discovery techniques, organizations stand to forfeit much of the value from the data they currently collect and store.
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Cites
An Introduction to Computational Learning Theory
Kearns, M. J., & Vazirani, U. (1994) · doi:10.7551/mitpress/3897.001.0001 · Cited by 1,697
Cited by
Geurts, P., Ernst, D., & Wehenkel, L. (2006) · Machine Learning · doi:10.1007/s10994-006-6226-1 · Cited by 9,300
(1999) · doi:10.1162/089976600300015565 · Cited by 2,799
Text classification from labeled and unlabeled documents using EM
Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (2000) · Machine Learning · doi:10.1023/a:1007692713085 · Cited by 2,736
(2000) · Cited by 1,467
Facial expression recognition from video sequences: temporal and static modeling
Cohen, I., Sebe, N., Garg, A., Chen, L. S., & Huang, T. S. (2003) · Computer Vision and Image Understanding · doi:10.1016/s1077-3142(03)00081-x · Cited by 874
Tsatsaronis, G., Balikas, G., Malakasiotis, P., Partalas, I., Zschunke, M., Alvers, M. R., Weissenborn, D., Krithara, A., Petridis, S., Polychronopoulos, D., et al. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0564-6 · Cited by 598
Wang, D., Miao, Q., & Pecht, M. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.03.129 · Cited by 351
Predicting human interruptibility with sensors: a Wizard of Oz feasibility study
Hudson, S., Fogarty, J., Atkeson, C., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J., & Yang, J. (2003) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/642611.642657 · Cited by 295
A tutorial introduction to the minimum description length principle
Grunwald, P. (2004) · arXiv · doi:10.48550/arxiv.math/0406077 · Cited by 293
Bird species recognition using support vector machines
Fagerlund, S. (2007) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2007/38637 · Cited by 276
A survey of Clustering Algorithms
Rokach, L. (2009) · Data Mining and Knowledge Discovery Handbook · doi:10.1007/978-0-387-09823-4_14 · Cited by 245
Baron-Cohen, S., Bowen, D. C., Holt, R. J., Allison, C., Auyeung, B., Lombardo, M. V., Smith, P., & Lai, M.-C. (2015) · PLOS ONE · doi:10.1371/journal.pone.0136521 · Cited by 238
Mohammed, M., Khan, M. B., & Bashier, E. B. M. (2016) · doi:10.1201/9781315371658 · Cited by 389
A systematic comparison of supervised classifiers
Amancio, D. R., Comin, C. H., Casanova, D., Travieso, G., Bruno, O. M., Rodrigues, F. A., & da Fontoura Costa, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094137 · Cited by 225
Mining query logs: turning search usage data into knowledge
Silvestri, F. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000013 · Cited by 194
Wanna improve process mining results?
Bose, R. P. J. C., Mans, R. S., & van der Aalst, W. M. P. (2013) · 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) · doi:10.1109/cidm.2013.6597227 · Cited by 192
Why Unbiased Computational Processes Can Lead to Discriminative Decision Procedures
Calders, T., & Žliobaitė, I. (2013) · Studies in Applied Philosophy, Epistemology and Rational Ethics · doi:10.1007/978-3-642-30487-3_3 · Cited by 128
ILP turns 20: Biography and future challenges
Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K., & Srinivasan, A. (2012) · Machine Learning · doi:10.1007/s10994-011-5259-2 · Cited by 126
Physical Activity Recognition from Accelerometer Data Using a Multi-Scale Ensemble Method
Zheng, Y., Won, W.-K., Guan, X., & Trost, S. (2013) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v27i2.18997 · Cited by 84
Aguiar, E., Chawla, N. V., Brockman, J., Ambrose, G. A., & Goodrich, V. (2014) · Proceedings of the Fourth International Conference on Learning Analytics And Knowledge · doi:10.1145/2567574.2567583 · Cited by 69
Challenges and opportunities in applied machine learning
Brodley, C. E., Rebbapragada, U., Small, K., & Wallace, B. C. (2012) · AI Magazine · doi:10.1609/aimag.v33i1.2367 · Cited by 32
A bias/variance decomposition for models using collective inference
Neville, J., & Jensen, D. (2008) · Machine Learning · doi:10.1007/s10994-008-5066-6 · Cited by 21
Yamamoto, Y., Tezuka, T., Jatowt, A., & Tanaka, K. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72524-4_28 · Cited by 14
(2012) · International Conference on Computational Linguistics
Metadata
- Authors
- Feyyad, U. M.
- Venue
- IEEE Expert
- Published
- 1996-10
- Volume
- 11
- Issue
- 5
- Pages
- 20-25
- ISSN
- 0885-9000 (ISSN-L)
- OpenAlex
- W2138745909
- Cited by
- 4,612
- References
- 1
- FWCI
- 111.68
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
