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

Q3 Business, Management and Accounting
C. Krubakaran, K. Venkatachalapathy
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引用次数: 0

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.
一种用于有效数据挖掘的新的学习和预测贝叶斯分层聚类Dirichlet混合模型
决策和业务支持是数据挖掘中的一个重要过程,可以通过模式分类和提取来实现。由于庞大的数据量需要匮乏的知识来处理,组织在解决这些问题时面临许多问题。聚类是一种有效的技术,可用于分析数据集并将其转换为有意义的模式。数据挖掘中的聚类使用各种属性来计算大型数据集并满足实时性问题。该模型将贝叶斯层次聚类模型与狄利克雷模型相结合,解决了大型数据集分析中的问题。实验结果表明,该模型比传统的全链路聚集聚类具有更好的聚类效率,聚类准确率达到92%。
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来源期刊
International Journal of Enterprise Network Management
International Journal of Enterprise Network Management Business, Management and Accounting-Management of Technology and Innovation
CiteScore
0.90
自引率
0.00%
发文量
28
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