A novel learning and prediction Bayesian hierarchical clustering-Dirichlet mixture model for effective data mining

C. Krubakaran and K. Venkatachalapathy

International Journal of Enterprise Network Management, 2020, vol. 11, issue 3, 251-263

Abstract: Decision making and business support is an important process in data mining and this can be achieved by means of pattern classification and extraction. Since the huge volume of data needs starving knowledge to process and organisation faces many issues in solving those issues. Clustering is an effective technology available to analyse and convert the datasets into meaningful patterns. Clustering in data mining uses various attributes to compute large dataset and meet out the real time issues. The proposed model uses Bayesian hierarchical clustering model with Dirichlet model to resolve the issues in large dataset analysis. Experimental results prove that proposed model experience better clustering efficiency than conventional complete link agglomerative clustering by achieving 92% of clustering accuracy.

Keywords: data mining; data clustering; Bayesian hierarchical clustering; BHC; Dirichlet. (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijenma:v:11:y:2020:i:3:p:251-263

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