MODIFIED ALTERNATIVE DECISION RULE IN THE PRE-CLUSTERING ALGORITHM

Volodymyr Mosorov

w.mosorow@kis.p.lodz.pl
Lodz University of Technology, Institute of Applied Computer Science (Poland)

Taras Panskyi


Lodz University of Technology, Institute of Applied Computer Science (Poland)

Sebastian Biedron


Lodz University of Technology, Institute of Applied Computer Science (Poland)

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.


Keywords:

pre-clustering, pre-cluster, decision rule

Aggarwal C.: Data Clustering: Algorithms and Applications 1st Edition, Chapman and Hall, 2013.
  Google Scholar

Gan G., Ma C., Wu G.: Data Clustering: Theory, Algorithms and Applications. ASA-SIAM Series on Statistics and Applied Probability, 2007.
  Google Scholar

Hofmann M., Klinkenberg R.: RapidMiner: Data Mining Use Cases and Business Analytics Applications, Chapman and Hall/CRC, 2013.
  Google Scholar

Jain A., Murthy M.: Flynn P.: Data Clustering: A Review. ACM Computing Surveys (CSUR), 1999.
  Google Scholar

Khan M.A.: H Pre-processing for K-means Clustering Algorithm. Senior Projects Spring, 2015.
  Google Scholar

Kovács L., Bednarik L.: Parameter Optimization for BIRCH Pre-Clustering Algorithm. 12th IEEE International Symposium on Computational Intelligence and Informatics, 2011.
  Google Scholar

Liu Y., Li Zh., Xiong H., Gao X., Wu J.: Understanding of Internal Clustering Validation Measures, IEEE International Conference on Data Mining, 2010.
  Google Scholar

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.
  Google Scholar

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.
  Google Scholar

Rokach L., Maimon.: Clustering Methods, Data Mining and Knowledge Discovery Handbook, 2005.
  Google Scholar

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Published
2016-05-10

Cited by

Mosorov, V. ., Panskyi, T. ., & Biedron, S. . (2016). MODIFIED ALTERNATIVE DECISION RULE IN THE PRE-CLUSTERING ALGORITHM. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 6(2), 9–12. https://doi.org/10.5604/20830157.1201309

Authors

Volodymyr Mosorov 
w.mosorow@kis.p.lodz.pl
Lodz University of Technology, Institute of Applied Computer Science Poland

Authors

Taras Panskyi 

Lodz University of Technology, Institute of Applied Computer Science Poland

Authors

Sebastian Biedron 

Lodz University of Technology, Institute of Applied Computer Science Poland

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