Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Soft Computing and Human-Centered Machines
Soft Computing and Human-Centered Machines
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Fuzzy multiset space and c-means clustering using Kernels with application to information retrieval
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
A partitive rough clustering algorithm
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
A framework for unsupervised selection of indiscernibility threshold in rough clustering
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
A fuzzy neighborhood model for clustering, classification, and approximations
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Lattice-valued hierarchical clustering for analyzing information systems
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Refinement Properties in Agglomerative Hierarchical Clustering
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
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Although the approaches are fundamentally different, the derivation of decision rules from information systems in the form of tables can be compared to supervised classification in pattern recognition; in the latter case classification rules should be derived from the classes of given points in a feature space. We also notice that methods of unsupervised classification (in other words, data clustering) in pattern recognition are closely related to supervised classification techniques. This observation leads us to the discussion of clustering for information systems by investigating relations between the two methods in the pattern classification. We thus discuss a number of methods of data clustering of information tables without decision attributes on the basis of rough set approach in this paper. Current clustering algorithms using rough sets as well as new algorithms motivated from pattern classification techniques are considered. Agglomerative clustering are generalized into a method of poset-valued clustering for discussing structures of information systems using new notations in relational databases. On the other hand K-means algorithms are developed using the kernel function approach. Illustrative examples are given.