Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
A GA-Based Clustering Algorithm for Large Data Sets with Mixed Numeric and Categorical Values
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
Immune K-means and negative selection algorithms for data analysis
Information Sciences: an International Journal
A novel clustering method based on SVM
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
A combinational clustering method based on artificial immune system and support vector machine
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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In the field of cluster analysis, objective function based clustering algorithm is one of widely applied methods so far However, this type of algorithms need the priori knowledge about the cluster number and the type of clustering prototypes, and can only process data sets with the same type of prototypes Moreover, these algorithms are very sensitive to the initialization and easy to get trap into local optima To this end, this paper presents a novel clustering method with fuzzy network structure based on limited resource to realize the automation of cluster analysis without priori information Since the new algorithm introduce fuzzy artificial recognition ball, operation efficiency is greatly improved By analyzing the neurons of network with minimal spanning tree, one can easily get the cluster number and related classification information The test results with various data sets illustrate that the novel algorithm achieves much more effective performance on cluster analyzing the large data set with mixed numeric values and categorical values.