Business data mining - a machine learning perspective

By Indranil Bose, RadhaKanta Mahapatra. Information Manager (The), 2001

432 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/57c095040066982614fe2789fe2db4fb93d3e3a3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Y. Wong
2. Sven F. Crone
3. Eric Wai Ting Ngai
4. Yijun Chen
5. Xin Sun
6. Robert Fildes
7. Konstantinos Nikolopoulos
8. Yong Hu

In Artificial Intelligence

1. Sven F. Crone
2. Konstantinos Nikolopoulos
3. Chen-Fu Chien
4. Eric Wai Ting Ngai
5. Robert Fildes
6. Ronald M. Summers
7. Ali Bou Nassif
8. Shijun Wang

In Information Systems

1. Chen-Fu Chien
2. Sven F. Crone
3. Eric Wai Ting Ngai
4. Ronald M. Summers
5. Ali Bou Nassif
6. Qassim Nasir
7. Shijun Wang
8. Yong Hu

Citations in the corpus

• Machine Learning and Radiology (2012, background): “…routinely used in banking (for detecting fraudulent transactions (Dorronsoro et al., 1997)), in finance (to predict stock prices (Huang et al., 2005a)), in marketing (to reveal patterns of consumer spending (Bose and Mahapatra, 2001)), and on the Internet (as part of search engines (Basili, 2003)).”
• Integrating AHP and data mining for product recommendation based on customer lifetime value (2005, neutral): “Data mining techniques [9] are a widely used information technology for extracting marketing knowledge and further supporting marketing decisions [4] [5] [33].”
• Data mining for yield enhancement in semiconductor manufacturing and an empirical study (2007, background): “In particular, Bose and Mahapatra (2001) conducted a series of data mining applications in many fields including finance, telecommunication, marketing, web analysis, and compared the strengths and weaknesses of various data mining techniques.”
• Forecasting and operational research: a review (2008, background): “Bose and Mahapatra (2001) provide a review of DM applications in business, and Shaw et al (2001) in marketing.”
• Machine Learning for Anomaly Detection: A Systematic Review (2021, background): “ML is the effort to ‘‘automate the process of knowledge acquisition from examples’’ [12].”
• The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature (2011, background): “Bose and Mahapatra [14] define data mining as a process of identifying interesting patterns in databases that can then be used in decisionmaking.”