We experience a daily reality such that we can be overpowered with data; in this manner it has turned out to be progressively critical to separate pertinent data from the unstable measure of information for. Information Mining is the iterative and intuitive procedure of finding substantial, novel, valuable, and justifiable examples or models in gigantic databases. Information Mining implies looking for important data in extensive volumes of information, utilizing investigation and examination, via programmed or self-loader implies, of expansive amounts of information so as to find significant examples and standards. Soft Computing (SC) alludes to methods of figuring in which accuracy is exchanged for tractability, heartiness and simplicity of usage. For the most part, SC encompasses the technologies of fuzzy logic, genetic algorithms, and neural networks, and it has emerged as an effective tool for dealing with data mining, control, modeling, and decision problems in complex systems. This is a review of the role of various soft-computing tools for different data mining tasks.We experience a daily reality such that we can be overpowered with data; in this manner it has turned out to be progressively critical to separate pertinent data from the unstable measure of information for. Information Mining is the iterative and intuitive procedure of finding substantial, novel, valuable, and justifiable examples or models in gigantic databases. Information Mining implies looking for important data in extensive volumes of information, utilizing investigation and examination, via programmed or self-loader implies, of expansive amounts of information so as to find significant examples and standards. Soft Computing (SC) alludes to methods of figuring in which accuracy is exchanged for tractability, heartiness and simplicity of usage. For the most part, SC encompasses the technologies of fuzzy logic, genetic algorithms, and neural networks, and it has emerged as an effective tool for dealing with data mining, control, modeling, and decision problems in complex systems. This is a review of t...
[1]
Sushmita Mitra,et al.
Neuro-fuzzy rule generation: survey in soft computing framework
,
2000,
IEEE Trans. Neural Networks Learn. Syst..
[2]
Sankar K. Pal,et al.
Fuzzy self-organization, inferencing, and rule generation
,
1996,
IEEE Trans. Syst. Man Cybern. Part A.
[3]
Do Phuc,et al.
Using Rough Genetic and Kohonen's Neural Network for Conceptual Cluster Discovery in Data Mining
,
1999,
RSFDGrC.
[4]
Gregory Piatetsky-Shapiro,et al.
The KDD process for extracting useful knowledge from volumes of data
,
1996,
CACM.
[5]
Lotfi A. Zadeh,et al.
Outline of a New Approach to the Analysis of Complex Systems and Decision Processes
,
1973,
IEEE Trans. Syst. Man Cybern..
[6]
Z. Pawlak.
Rough Sets: Theoretical Aspects of Reasoning about Data
,
1991
.
[7]
Sankar K. Pal,et al.
Fuzzy multi-layer perceptron, inferencing and rule generation
,
1995,
IEEE Trans. Neural Networks.
[8]
Johannes Fürnkranz,et al.
Knowledge Discovery in International Conflict Databases
,
1997,
Appl. Artif. Intell..
[9]
Wojciech Ziarko,et al.
DATA‐BASED ACQUISITION AND INCREMENTAL MODIFICATION OF CLASSIFICATION RULES
,
1995,
Comput. Intell..
[10]
Abraham Silberschatz,et al.
What Makes Patterns Interesting in Knowledge Discovery Systems
,
1996,
IEEE Trans. Knowl. Data Eng..
[11]
Witold Pedrycz,et al.
Data Mining Methods for Knowledge Discovery
,
1998,
IEEE Trans. Neural Networks.
[12]
Sankar K. Pal,et al.
Rough fuzzy MLP: knowledge encoding and classification
,
1998,
IEEE Trans. Neural Networks.