Algorithms for clustering data
Algorithms for clustering data
Symbolic clustering using a new dissimilarity measure
Pattern Recognition
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
A computer generated aid for cluster analysis
Communications of the ACM
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual cluster validity for prototype generator clustering models
Pattern Recognition Letters
Cluster Validation with Generalized Dunn's Indices
ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expanding self-organizing map for data visualization and cluster analysis
Information Sciences: an International Journal - Special issue: Soft computing data mining
Entropy-based criterion in categorical clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Minimum Entropy Clustering and Applications to Gene Expression Analysis
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
On FastMap and the Convex Hull of Multivariate Data: Toward Fast and Robust Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relational visual cluster validity (RVCV)
Pattern Recognition Letters
Establishing performance evaluation structures by fuzzy relation-based cluster analysis
Computers & Mathematics with Applications
Interval-valued fuzzy relation-based clustering with its application to performance evaluation
Computers & Mathematics with Applications
IEEE Transactions on Signal Processing
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
Robust fuzzy clustering of relational data
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Invertible matrices and semilinear spaces over commutative semirings
Information Sciences: an International Journal
Constructing and mapping fuzzy thematic clusters to higher ranks in a taxonomy
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Information Sciences: an International Journal
Individual difference of artificial emotion applied to a service robot
Frontiers of Computer Science in China
Developing additive spectral approach to fuzzy clustering
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Fuzzy relational clustering around medoids: A unified view
Fuzzy Sets and Systems
Thematic fuzzy clusters with an additive spectral approach
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Information Sciences: an International Journal
A clustering algorithm for multiple data streams based on spectral component similarity
Information Sciences: an International Journal
Hi-index | 0.07 |
The first stage of organizing objects is to partition them into groups or clusters. The clustering is generally done on individual object data representing the entities such as feature vectors or on object relational data incorporated in a proximity matrix. This paper describes another method for finding a fuzzy membership matrix that provides cluster membership values for all the objects based strictly on the proximity matrix. This is generally referred to as relational data clustering. The fuzzy membership matrix is found by first finding a set of vectors that approximately have the same inter-vector Euclidian distances as the proximities that are provided. These vectors can be of very low dimension such as 5 or less. Fuzzy c-means (FCM) is then applied to these vectors to obtain a fuzzy membership matrix. In addition two-dimensional vectors are also created to provide a visual representation of the proximity matrix. This allows comparison of the result of automatic clustering to visual clustering. The method proposed here is compared to other relational clustering methods including NERFCM, Rouben's method and Windhams A-P method. Various clustering quality indices are also calculated for doing the comparison using various proximity matrices as input. Simulations show the method to be very effective and no more computationally expensive than other relational data clustering methods. The membership matrices that are produced by the proposed method are less crisp than those produced by NERFCM and more representative of the proximity matrix that is used as input to the clustering process.