MODIFIED ALTERNATIVE DECISION RULE IN THE PRE-CLUSTERING ALGORITHM
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Abstract
In this paper the pre-clustering algorithm with the modified decision rule has been presented. The application of pre-clustering algorithm answers the question whether to carry out the clustering or would it result in the appearance of artificial structure (input data is one cluster and it is unnecessary to divide it). The versatility and simplicity of this algorithm allows using it in a various fields of science and technology. The pros and cons of pre-clustering algorithm have been also considered.
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References
Aggarwal C.: Data Clustering: Algorithms and Applications 1st Edition, Chapman and Hall, 2013.
Gan G., Ma C., Wu G.: Data Clustering: Theory, Algorithms and Applications. ASA-SIAM Series on Statistics and Applied Probability, 2007.
Hofmann M., Klinkenberg R.: RapidMiner: Data Mining Use Cases and Business Analytics Applications, Chapman and Hall/CRC, 2013.
Jain A., Murthy M.: Flynn P.: Data Clustering: A Review. ACM Computing Surveys (CSUR), 1999.
Khan M.A.: H Pre-processing for K-means Clustering Algorithm. Senior Projects Spring, 2015.
Kovács L., Bednarik L.: Parameter Optimization for BIRCH Pre-Clustering Algorithm. 12th IEEE International Symposium on Computational Intelligence and Informatics, 2011.
Liu Y., Li Zh., Xiong H., Gao X., Wu J.: Understanding of Internal Clustering Validation Measures, IEEE International Conference on Data Mining, 2010.
McCallum A., Nigam K., Ungar L.H.: Efficient Clustering of High Dimensional Data Sets with Application to Reference Matching, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 169–178.
Mosorov V., Tomczak L.: Image Texture Defect Detection Method Using Fuzzy C–Means Clustering for Visual Inspection Systems, Arabian Journal for Science and Engineering, 2014.
Rokach L., Maimon.: Clustering Methods, Data Mining and Knowledge Discovery Handbook, 2005.
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