X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Data Mining: A Knowledge Discovery Approach
Data Mining: A Knowledge Discovery Approach
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Ant based clustering of time series discrete data --- a rough set approach
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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In the paper, we attempt to identify the crucial determinants of innovativeness economy and the correlations between the determinants. We based our research on the Innovativeness Union Scoreboard IUS dataset. In order to solve the problem, we propose to use the Double Self-Organizing Feature Map SOM approach. In the first step, countries, described by determinants of innovativeness economy, are clustered using SOMs according to five year time series for each determinant separately. In the second step, results of the first step are clustered again using SOM to obtain the final correlation represented in the form of a minimal spanning tree. We propose some modifications of the clustering process using SOMs to improve classification results and efficiency of the learning process.