Uncertainly measures of rough set prediction
Artificial Intelligence
Dynamic fuzzy data analysis based on similarity between functions
Fuzzy Sets and Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Evolutionary and adaptive synthesis methods
Formal engineering design synthesis
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Some refinements of rough k-means clustering
Pattern Recognition
Web Intelligence and Agent Systems
Editorial: Special issue on soft computing for dynamic data mining
Applied Soft Computing
Info-fuzzy algorithms for mining dynamic data streams
Applied Soft Computing
Dynamic data assigning assessment clustering of streaming data
Applied Soft Computing
A Dynamic Approach to Rough Clustering
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy Sets and Systems
Shadowed c-means: Integrating fuzzy and rough clustering
Pattern Recognition
A comparative study of fuzzy sets and rough sets
Information Sciences: an International Journal
Toward a generalized theory of uncertainty (GTU)--an outline
Information Sciences: an International Journal
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences: an International Journal
Rough neuro-fuzzy structures for classification with missing data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary rough k-medoid clustering
Transactions on rough sets VIII
Online phishing classification using adversarial data mining and signaling games
ACM SIGKDD Explorations Newsletter
Fuzzy-rough sets for information measures and selection of relevant genes from microarray data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Shadowed sets in the characterization of rough-fuzzy clustering
Pattern Recognition
A class of dynamic rough partitive algorithms
International Journal of Intelligent Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Roughness bounds in rough set operations
Information Sciences: an International Journal
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic cluster generation for a fuzzy classifier with ellipsoidalregions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Dynamic clustering with soft computing
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
An extension to rough c-means clustering algorithm based on boundary area elements discrimination
Transactions on Rough Sets XVI
An extension to Rough c-means clustering based on decision-theoretic Rough Sets model
International Journal of Approximate Reasoning
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Dynamic data mining has gained increasing attention in the last decade. It addresses changing data structures which can be observed in many real-life applications, e.g. buying behavior of customers. As opposed to classical, i.e. static data mining where the challenge is to discover pattern inherent in given data sets, in dynamic data mining the challenge is to understand - and in some cases even predict - how such pattern will change over time. Since changes in general lead to uncertainty, the appropriate approaches for uncertainty modeling are needed in order to capture, model, and predict the respective phenomena considered in dynamic environments. As a consequence, the combination of dynamic data mining and soft computing is a very promising research area. The proposed algorithm consists of a dynamic clustering cycle when the data set will be refreshed from time to time. Within this cycle criteria check if the newly arrived data have structurally changed in comparison to the data already analyzed. If yes, appropriate actions are triggered, in particular an update of the initial settings of the cluster algorithm. As we will show, rough clustering offers strong tools to detect such changing data structures. To evaluate the proposed dynamic rough clustering algorithm it has been applied to synthetic as well as to real-world data sets where it provides new insights regarding the underlying dynamic phenomena.