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A Comparative Study in Data Mining: Clustering and Classification Capabilities

机译:数据挖掘的比较研究:聚类和分类能力

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摘要

The ICT evolution has driven on the creation of a capable society, in providing new kinds and type of information. The gathered information is stored continuously, meaning that a great amount of databases has to be created. The problem that arises is whether there is a global manner of managing and gaining knowledge out of the rising variety and volumes of data. Many efforts have been developed for addressing the emerging challenges of data mining based on statistics and machine learning techniques that can significantly boost the ability to analyze data. In this paper, a detailed study on the data mining field takes place, followed by a comparative study between clustering and classification techniques, resulting that the integration of clustering and classification techniques can provide more accurate results than a simple classification technique that classifies datasets with priorly known attributes and classes.
机译:ICT的发展推动了一个有能力的社会的创建,从而提供了新型和新型的信息。收集的信息将连续存储,这意味着必须创建大量的数据库。出现的问题是,是否存在一种全球性的方式来管理和从不断增加的数据种类和数量中获取知识。为了解决基于统计和机器学习技术的数据挖掘新兴挑战,已经做出了许多努力,可以显着提高分析数据的能力。在本文中,对数据挖掘领域进行了详细的研究,然后对聚类和分类技术进行了比较研究,结果表明,聚类和分类技术的集成可以提供比以前对数据集进行分类的简单分类技术更准确的结果已知的属性和类。

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