A New Adaptive Elastic Net Method for Cluster Analysis
AbstractClustering is inherently a highly challenging research problem. The elastic net algorithm is designed to solve the traveling salesman problem initially, now is verified to be an efficient tool for data clustering in n-dimensional space. In this paper, by introducing a nearest neighbor learning method and a local search preferred strategy, we proposed a new Self-Organizing NN approach, called the Adaptive Clustering Elastic Net (ACEN) to solve the cluster analysis problems. ACEN consists of the adaptive clustering elastic net phase and a local search preferred phase. The first phase is used to find a cyclic permutation of the points as to minimize the total distances of the adjacent points, and adopts the Euclidean distance as the criteria to assign each point. The local search preferred phase aims to minimize the total dissimilarity within each clusters. Simulations were made on a large number of homogeneous and nonhomogeneous artificial clusters in n dimensions and a set of publicly standard problems available from UCI. Simulation results show that compared with classical partitional clustering methods, ACEN can provide better clustering solutions and do more efficiently.
 Baldi P., Hatfield G.W.(2002); DNA Microarrays and Gene Expression, Cambridge University Press, 2002.
 Climer S., Weixiong Zhang W. (2006); Rearrangement Clustering: Pitfalls, Remedies, and Applications, Journal of Machiner Learning Research, 7, 919-943, 2006.
 Dittenbach M., Rauber A. (2002); Uncovering Hierarchical Structure in Data Using the Growing Hierarchical Self-organizing Map, Neurocomputing, 48, 199-216, 2002.
 Durbin R., Willshaw D. (1987); An Analogue Approach to the Traveling Salesman Problem Using an Elastic Net Approach, Nature, 326, 689-691, 1987.
 Frigui H., Krishnapuram R. (1999); A Robust Competitive Clustering Algorithm with Applications in Computer Vision, IEEE Trans. Pattern Analysis and Machine Intelligence, 21(5), 450-465, 1999.
 Gorbunov S., Kisel I. (2006); Elastic Net for Standalone RICH Ring Finding, Proceedingspublished in NIM, 559, 139-142, 2006.
 Jain A.K., Flynn P. (1996); Image Segmentation Using Clustering, Advances in Image Understanding, 65-83, 1996.
 Jain K. (2010); Data Clustering: 50 Years Beyond K-Means, Pattern Recognition Letters, 31(8), 651-666, 2010.
 Kantardzic M. (2011); Data Mining: Concepts, Models, Methods, and Algorithms, Wiley- IEEE Press, 2011.
 Kohonen T. (1982); Self-organized Formation of the Topologically Correct Feature Maps, Biological Cybernetics, 43, 59-69, 1982.
 Kohonen T. (2001); Self-Organizing Maps, 3rd Ed. New York: Springer-Verlag, 2001.
 Levano M.; Hans Nowak H. (2011); New Aspects of the Elastic Net Algorithm for Cluster Analysis, Neural Comput and Applic, 20, 835-850, 2011.
 Raube A., Merkl D. (2002); The Growing Hierarchical Self-Organizing Map: Exploratory Analysis of High-Dimensional Data, IEEE Transactions on Neural Networks, 13(6), 1331- 1340, 2002.
 Rose K., Gurewitz E., Fox G. (1990); Statistical Mechanics and Phase Transitions in Clustering, Phys Rev Lett, 65, 945-948, 1990.
 Saric T.; Simunovic, G. (2016); Estimation of Machining Time for CNC Manufacturing Using Neural Computing, International Journal of Simulation Modelling, 15(4), 663-675, 2016.
 Shi J., Malik J. (2000); Normalized Cuts and Image Segmentation, IEEE Trans. Pattern Analysis and Machine Intelligence, 22(8), 888-905, 2000.
 Tang M., Gong D., Liu S., Zhang H. (2016); Applying Multi-phase Particle Swarm Optimization to Solve Bulk Cargo Port Scheduling Problem, Advances in Production Engineering and Management, 11(4), 299-310, 2016.
 Tavazoie S., Hughes D.; Campbell M.J., Cho R.J., Church G.M. (1999); Systematic Determination of Genetic Network Architecture, Nature Genetic, 22, 281-285, 1999.
 Vakhutinsky A.I., Golden B.L. (2003); The Co-adaptive Neural Network Approach to the Euclidean Traveling Salesman Problem, Neural Networks, 16(10), 1499-1525, 2003.
 Wang J., Tang Z., Qiping Cao, Xinshun Xu (2003); An Elastic Net Learning Algorithm for Edge Linking of Images, IEICE Trans. Fundamentals, E86-A(11), 2879-2886, 2003.
 Wu S., Liew A.W.C., Yan, H., Yang M. (2014); Cluster Analysis of Gene Expression Database on Self-Splitting and Merging Competitive Learning, IEEE Trans. Information Technology in Biomedicine, doi: 10.1109/TITB.2004.824724, 8(1), 5-15, 2014.
 Xu R. (2005); Survey of Clustering Algorithm, IEEE Transaction On Neural Networks, 16, 645-678, 2005.
 Yang K. W. (2015); A Variables Clustering Based Differential Evolution Algorithm to Solve Production Planning Problem, International Journal of Simulation Modelling, 14(3), 525- 538, 2015.
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