Data mining with neural networks: solving business problems from application development to decision support
Fuzzy clustering procedures for conical fuzzy vector data
Fuzzy Sets and Systems
Fuzzy sets and their application to clustering and training
Fuzzy sets and their application to clustering and training
Fuzzy clustering based on k-nearest-neighbours rule
Fuzzy Sets and Systems - Special issue on clustering and learning
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Cybernetics and Systems Analysis
Techniques of Cluster Algorithms in Data Mining
Data Mining and Knowledge Discovery
Efficient Density-Based Clustering of Complex Objects
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Effective and Efficient Distributed Model-Based Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
An investigation of mountain method clustering for large data sets
Pattern Recognition
Validity-guided (re)clustering with applications to image segmentation
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Clustering algorithm for intuitionistic fuzzy sets
Information Sciences: an International Journal
PFHC: A clustering algorithm based on data partitioning for unevenly distributed datasets
Fuzzy Sets and Systems
Robustness of density-based clustering methods with various neighborhood relations
Fuzzy Sets and Systems
Comparative clustering analysis of bispectral index series of brain activity
Expert Systems with Applications: An International Journal
Adjusting the clustering results referencing an external set
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Expert Systems with Applications: An International Journal
Fuzzy and crisp clustering methods based on the neighborhood concept: A comprehensive review
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS'2011: 2nd International Fuzzy Systems Symposium
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In this paper, a new level-based (hierarchical) approach to the fuzzy clustering problem for spatial data is proposed. In this approach each point of the initial set is handled as a fuzzy point of the multidimensional space. Fuzzy point conical form, fuzzy α-neighbor points, fuzzy α-joint points are defined and their properties are explored. It is known that in classical fuzzy clustering the matter of fuzziness is usually a possibility of membership of each element into different classes with different positive degrees from [0,1]. In this study, the fuzziness of clustering is evaluated as how much in detail the properties of classified elements are investigated. In this extent, a new Fuzzy Joint Points (FJP) method which is robust through noises is proposed. Algorithm of FJP method is developed and some properties of the algorithm are explored. Also sufficient condition to recognize a hidden optimal structure of clusters is proven. The main advantage of the FJP algorithm is that it combines determination of initial clusters, cluster validity and direct clustering, which are the fundamental stages of a clustering process. It is possible to handle the fuzzy properties with various level-degrees of details and to recognize individual outlier elements as independent classes by the FJP method. This method could be important in biological, medical, geographical information, mapping, etc. problems.