The Role of Visual Assessment of Clusters for Big Data Analysis: From Real-World Internet of Things

IF 1.9 Q3 COMPUTER SCIENCE, CYBERNETICS

IEEE Systems Man and Cybernetics Magazine Pub Date : 2020-10-01 DOI:10.1109/MSMC.2019.2961160

M. Palaniswami, A. S. Rao, Dheeraj Kumar, Punit Rathore, S. Rajasegarar

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引用次数: 6

Abstract

The Internet of Things (IoT) is playing a vital role in shaping today?s technological world, including our daily lives. By 2025, the number of connected devices due to the IoT is estimated to surpass a whopping 75 billion. It is a challenging task to discover, integrate, and interpret processed big data from such ubiquitously available heterogeneous and actively natural resources and devices. Cluster analysis of IoT-generated big data is essential for the meaningful interpretation of such complex data. However, we often have very limited knowledge of the number of clusters actually present in the given data. The problem of finding whether clusters are present even before applying clustering algorithms is termed the assessment of clustering tendency. In this article, we present a set of useful visual assessment of cluster tendency (VAT) tools and techniques developed with major contributions from James C. Bezdek. The article further highlights how these techniques are advancing the IoT through large-scale IoT implementations.

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集群可视化评估在大数据分析中的作用:来自现实世界的物联网

物联网(IoT)在塑造当今世界的过程中发挥着至关重要的作用。科技世界，包括我们的日常生活。到2025年，物联网连接设备的数量预计将超过750亿。从这种无处不在的异构和活跃的自然资源和设备中发现、整合和解释处理过的大数据是一项具有挑战性的任务。对物联网生成的大数据进行聚类分析对于有意义地解释此类复杂数据至关重要。然而，我们通常对给定数据中实际存在的簇的数量知之甚少。在应用聚类算法之前发现聚类是否存在的问题被称为聚类倾向的评估。在本文中，我们提出了一套有用的集群趋势(VAT)可视化评估工具和技术，这些工具和技术是由James C. Bezdek的主要贡献开发的。本文进一步强调了这些技术如何通过大规模物联网实施来推进物联网。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Systems Man and Cybernetics Magazine COMPUTER SCIENCE, CYBERNETICS-

自引率

6.20%

发文量