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

机译:一种新的学习和预测贝叶斯分层聚类 - 用于有效数据挖掘的Dirichlet混合模型

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摘要

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.
机译:决策和业务支持是数据挖掘中的一个重要过程,这可以通过模式分类和提取来实现。由于大量的数据需求匮乏地匮乏流程和组织面临着解决这些问题的许多问题。聚类是一种有效的技术,可分析并将数据集转换为有意义的模式。数据挖掘中的群集使用各种属性来计算大型数据集并满足实时问题。该建议的模型使用Dirichlet模型的Bayesian分层群集模型来解决大型数据集分析中的问题。实验结果证明,通过实现92%的聚类精度来实现比传统的完整链接凝聚聚类更好的聚类效率。

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