Data mining in business services
Integrity signals
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AI techniques, including deep learning, are operated to detect hidden patterns in large data sets.
“AI techniques, including deep learning, are operated to detect hidden patterns in large data sets [236].”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [236]
Cites
Business data mining — a machine learning perspective
Bose, I., & Mahapatra, R. (2001) · Information & Management · doi:10.1016/s0378-7206(01)00091-x · Cited by 437 · see citing works
Introduction to Business Data Mining
Olson, D. L. (2005) · Cited by 395 · see citing works
Self-citationDynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction
Sung, T. K., Chang, N., & Lee, G. (1999) · Journal of Management Information Systems · doi:10.1080/07421222.1999.11518234 · Cited by 238 · see citing works
Data mining: manufacturing and service applications
Kusiak, A. (2006) · International Journal of Production Research · doi:10.1080/00207540600632216 · Cited by 139 · see citing works
An analysis of customer retention and insurance claim patterns using data mining: a case study
Smith, K. A., Willis, R. J., & Brooks, M. (2000) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2600941 · Cited by 137 · see citing works
Efficient mining and prediction of user behavior patterns in mobile web systems
Tseng, V. S., & Lin, K. W. (2006) · Information and Software Technology · doi:10.1016/j.infsof.2005.12.014 · Cited by 101 · see citing works
Knowledge discovery techniques for predicting country investment risk
Becerra‐Fernandez, I., Zanakis, S. H., & Walczak, S. M. (2002) · Computers & Industrial Engineering · doi:10.1016/s0360-8352(02)00140-7 · Cited by 75 · see citing works
Targeting Customers with Statistical and Data-Mining Techniques
Drew, J. H., Mani, D. R., Betz, A. L., & Datta, P. (2001) · Journal of Service Research · doi:10.1177/109467050133002 · Cited by 71 · see citing works
Data Mining in Finance: Advances in Relational and Hybrid Methods
Cowan, A. M. (2002) · International Journal of Forecasting · doi:10.1016/s0169-2070(01)00128-5 · Cited by 54 · see citing works
Using data mining for customer satisfaction research
Garver, M. S. (2002) · Cited by 48 · see citing works
Zurada, J. M., & Lonial, S. C. (2011) · Journal of Applied Business Research (JABR) · doi:10.19030/jabr.v21i2.1488 · Cited by 43 · see citing works
Data mining for financial decision making
Wang, H., & Weigend, A. S. (2003) · Decision Support Systems · doi:10.1016/s0167-9236(03)00090-3 · Cited by 24 · see citing works
Mining for classes and patterns in behavioural data
Adams, N. M., Hand, D. J., & Till, R. J. (2001) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2601202 · Cited by 21 · see citing works
Data Mining for Financial Decision Making
Wang, H., & Weigend, A. S. (2002) · Decision Support Systems · doi:10.1016/s0167-9236(02)00021-0 · Cited by 21 · see citing works
Technology-mining model concerning operation characteristics of technology and service providers
Hou, J. L., & Yang, S.-Y. (2006) · International Journal of Production Research · doi:10.1080/00207540500490996 · Cited by 7 · see citing works
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Hassani, S., & Dackermann, U. (2023) · Sensors · doi:10.3390/s23042204 · Cited by 492 · see citing works
Hassani, S., & Dackermann, U. (2023) · Sensors · doi:10.3390/s23063293 · Cited by 146 · see citing works
Keshmiry, A., Hassani, S., Mousavi, M., & Dackermann, U. (2023) · Buildings · doi:10.3390/buildings13040918 · Cited by 96 · see citing works
- Authors
- Olson, D. L.
- Affiliations
- University of Nebraska–Lincoln (US)
- Corresponding author
- Olson, David L.
- ORCID
- Olson, David L.
- Venue
- Service Business
- Published
- 2006-12-13
- Volume
- 1
- Issue
- 3
- Pages
- 181-193
- ISSN
- 1862-8508 (ISSN-L)
- 1862-8516
- OpenAlex
- W1990496762
- Cited by
- 34
- References
- 15
- Citations by year
- 2024: 2 · 2023: 5 · 2022: 6 · 2021: 2 · 2020: 2 · 2019: 4 · 2018: 2 · 2017: 1 · 2016: 1 · 2014: 2 · 2012: 5
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Service Business (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Keywords
- data mining, business services, service sector, business functions, data mining techniques, analytical tools, data mining tools, big data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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