Data mining in soft computing framework: a survey

By S. Mitra, Sankar Kumar Pal, Pabitra Mitra. IEEE Trans. Neural Networks, 2002

720 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

View Paper: https://www.semanticscholar.org/paper/ee43d8809b79808e3073d9c7b1c3c64a7317b25a

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Citations in the corpus

Web mining in soft computing framework: relevance, state of the art and future directions (2002, background): “A recent review [51] is a testimony in this regard.”
Support vector machines with symbolic interpretation (2002, methodology): “These rule extraction methods serve for several purposes: to provide NNs with explanatory power, knowledge acquisition for symbolic AI systems, data exploration, development of hybrid architectures [13] and improved adequacy for data mining applications [8],[14].”
Fuzzy min–max neural networks for categorical data: application to missing data imputation (2012, background): “It has the generic benefits of neural networks—like massive parallelism and robustness—and, at the same time, uses fuzzy logic to model vague or qualitative knowledge and convey uncertainty [15].”
A survey of Knowledge Discovery and Data Mining process models (2006, methodology): “A similar overview , but concentrating on the evaluation of rules , is given by Mitra et al .”
Data mining in manufacturing: a review based on the kind of knowledge (2009, neutral): “Data mining is a particular step in this process, involving the application of specific algorithms for extracting patterns(models) from data.”
Application of data mining techniques in customer relationship management: A literature review and classification (2009, methodology): “(v) Regression: Regression is a kind of statistical estimation technique used to map each data object to a real value provide prediction value (Carrier & Povel, 2003; Mitra et al., 2002).”
Rule-Based Learning Systems for Support Vector Machines (2006, neutral): “With the aim of making the resulting classifier system interpretable, in the last 15 years rule extraction methods for trained neural networks have been developed [1, 5, 15, 22, 25].”
A Review of Financial Accounting Fraud Detection based on Data Mining Techniques (2012, neutral): “1, is based on a literature review of existing knowledge on the nature of data mining research [19,20], fraud detection research [1,3,16,17,18].”
The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature (2011, background): “2, is based on a literature review of existing knowledge on the nature of data mining research [3,52], fraud detection research [13,24,58,86,88], and the financial crime framework of the U.”