ACM Computing Surveys (CSUR)
Evaluating document clustering for interactive information retrieval
Proceedings of the tenth international conference on Information and knowledge management
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
Elastic Neural Net Algorithm for Cluster Analysis
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An analysis of the elastic net approach to the traveling salesman problem
Neural Computation
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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This paper presents an effective elastic net-based clustering algorithm for complex and non-linearly separable data. The basic idea of the proposed algorithm is simple and can be summarized into two steps: (1) assign patterns to groups based on the attraction and tension between the elastic nodes in a ring and neighbors of the patterns and (2) merge the groups based on the distance between the elastic nodes. To evaluate the performance of the proposed method, we compare it with several state-of-the-art clustering methods in solving the data clustering problem. The simulation results show that the proposed algorithm can provide much better results than the other clustering algorithms compared in terms of the accuracy rate. The results also show that the proposed algorithm works well for complex datasets, especially non-linearly separable data.