A link between k nearest neighbour rules and knowledge based systems by squence analysis
Pattern Recognition Letters
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A study of instance-based algorithms for supervised learning tasks: mathematical, empirical, and psychological evaluations
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
A supervised growing neural gas algorithm for cluster analysis
International Journal of Hybrid Intelligent Systems
A supervised growing neural gas algorithm for cluster analysis
International Journal of Hybrid Intelligent Systems
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In this paper, we present a new algorithm based on the nearest neighbours method, for discovering groups and identifying interesting distributions in the underlying data in the labelled databases. We introduces the theory of nearest neighbours sets in order to base the algorithm S-NN (Similar Nearest Neighbours). Traditional clustering algorithms are very sensitive to the user-defined parameters and an expert knowledge is required to choose the values. Frequently, these algorithms are fragile in the presence of outliers and any adjust well to spherical shapes. Experiments have shown that S-NN is accurate discovering arbitrary shapes and density clusters, since it takes into account the internal features of each cluster, and it does not depend on a user-supplied static model. S-NN achieve this by collecting the nearest neighbours with the same label until the enemy is found (it has not the same label). The determinism and the results offered to the researcher turn it into a valuable tool for the representation of the inherent knowledge to the labelled databases.