音乐数据挖掘的大数据可视化和可视化分析

Katrina E. Barkwell, A. Cuzzocrea, C. Leung, Ashley A. Ocran, Jennifer M. Sanderson, J. Stewart, Bryan H. Wodi
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引用次数: 18

摘要

由于现在可以很容易地生成或高速收集大量不同真实性的各种有价值的数据,因此在各种实际应用中都需要大数据可视化和可视化分析。音乐数据是大数据的一个例子。这些大数据中蕴含着有用的信息和有价值的知识。许多现有的大数据挖掘算法以文本或表格的形式返回有用的信息和有价值的知识。知道“一张图片胜过千言万语”,大数据可视化和可视化分析也很有需求。在本文中,我们提出了一个可视化和分析大数据的系统。特别是,我们的系统专注于从音乐数据中发现和探索频繁模式(即频繁出现在一起的项目集合)的大数据科学任务。评价结果表明了该系统在音乐数据挖掘的大数据可视化和可视化分析中的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Big Data Visualisation and Visual Analytics for Music Data Mining
As high volumes of a wide variety of valuable data of different veracities can be easily generated or collected at a high velocity nowadays, big data visualisation and visual analytics are in demand in various real-life applications. Musical data are examples of big data. Embedded in these big data are useful information and valuable knowledge. Many existing big data mining algorithms return useful information and valuable knowledge in textual or tabular forms. Knowing that "a picture is worth a thousand words", big data visualisation and visual analytics are also in demand. In this paper, we present a system for visualising and analysing big data. In particular, our system focuses on the big data science task of the discovery and exploration of frequent patterns (i.e., collections of items that frequently occurring together) from musical data. Evaluation results show the applicability of our system in big data visualisation and visual analytics for music data mining.
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