Results 131 to 140 of about 9,792 (165)

Fuzzy data mining for customer loyalty analysis

9th International Conference on Information Technology (ICIT'06), 2006
One of the major conceptual analyses in analytical CRM is customer loyalty. Current methods use user defined and statistical methods for loyalty analysis. One of the major limitations of these methods is that they assign every customer into exactly one segment, which may be counter intuitive.
Jay B. Simha, S. S. Iyengar 0001
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Why fuzzy in data mining?

Fourth International Symposium on Uncertainty Modeling and Analysis, 2003. ISUMA 2003., 2004
Fuzzy - as has been emphasized by Zadeh - is a human interface, a link between human judgement and machine information processing. It represents an approach for experimentation with the sensitivity of human classification, i.e. the notion of adding tacit semantic knowledge (Polanyi) to search procedures in data banks.
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Application of fuzzy rule induction to data mining

1998
In this paper, a data mining process to induce a set of fuzzy rules from a database is presented. This process is based on the construction of fuzzy decision trees. We present a method to construct fuzzy decision trees and a method to use them to classify new examples. In presence of databases, prerequisites for training sets are introduced to generate
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Fuzzy mining of meteorological data

1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.98TH8353), 2002
The process of knowledge discovery is essentially that of randomization. The key to discovery lies in the successful mining of non-trivial attributed databases. Randomizing declarative data for procedural knowledge (i.e., the theory) will necessarily be limited to the syntax of if-then rules. A logic for randomization underpins every theorem defined in
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MOGA for multi-level fuzzy data mining

2012 IEEE International Conference on Granular Computing, 2012
In this paper, we propose a Multi-Objective Multi-Level Genetic-Fuzzy Mining (MOMLGFM) algorithm for mining a set of non-dominated membership functions for mining multi-level fuzzy association rules. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy.
Chun-Hao Chen, Chi-Hsuan Ho, Tzung-Pei Hong, Wei-Tee Lin   +3 more
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Fuzzy Utility Mining on Temporal Data

2022 4th International Conference on Data Intelligence and Security (ICDIS), 2022
Zhenqiang Ye, Shicheng Wan, Wensheng Gan, Jiahui Chen 0002, Linlin Tang   +4 more
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Preface: Fuzziness and Uncertainty in Data Mining

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2022
Vicenç Torra, Yasuo Narukawa
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Fuzzy Data Mining in Higher Dimensions for Data Analysis

2007 IEEE International Conference on Information Reuse and Integration, 2007
To extract fuzzy rules from databases or data files, we first select a set of attributes to associate and restrict all records (rows) to these. We next embed these feature vectors of small dimension into a high dimensional feature space by a Gaussian kernel mapping that yields a symmetric fuzzy membership matrix. The entry value at row i and column] is
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Fuzzy Methods in Data Mining

2009
Tools and techniques that have been developed during the last 40 years in the field of fuzzy set theory (FST) have been applied quite successfully in a variety of application areas. A prominent example of the practical usefulness of corresponding techniques is fuzzy control, where the idea is to represent the input-output behaviour of a controller (of ...
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An Improved Ant Algorithm for Fuzzy Data Mining

2010
In the past, two mining algorithms were proposed to find suitable membership functions for fuzzy association rules based on the ant colony systems. In the two approaches, the coding of the possible solutions is by binary strings, which form a discrete solution space.
Min-Thai Wu, Tzung-Pei Hong, Chung-Nan Lee   +2 more
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