社交媒体分类数据与数值数据聚类性能的实证研究与比较分析

IF 0.6 4区 综合性期刊 Q3 MULTIDISCIPLINARY SCIENCES Acta Scientiarum-technology Pub Date : 2022-03-11 DOI:10.4025/actascitechnol.v44i1.58653
Dr. Shini Renjith, A. Sreekumar, M. Jathavedan
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引用次数: 1

摘要

社交媒体极大地影响了现代生活方式和大多数行业的经营方式。社交媒体数据是指用户在社交互动过程中以文字、声音、视觉等形式创造的内容。它现在已经发展成为零售、营销、广告、旅游、酒店、教育等不同垂直行业的主要信息来源。庞大的数据量导致需要更好和有效的个性化信息检索程序。传统的基于内容和/或协同过滤的数据挖掘和信息检索技术被证明计算成本高,而且相对于它必须处理的数据量,可扩展性较差。采用集群技术是解决这个问题的一个潜在解决方案,因为它可以最大限度地减少推荐系统等工业应用程序中需要管理的数据量。本实证研究侧重于评估多种聚类算法,目的是为从社交媒体来源中提取的数值数据找到聚类的理想解决方案。作为这项工作的一部分,实验中使用了三个不同的公共数据集,这些数据集具有不同数量的属性和来自旅游领域的记录
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An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media
Social media has significantly influenced modern lifestyle and the way in which most of the industries operate their business. Social media data refers to the contents created by users during their social interactions in the form of text, sound, visuals, etc. It has now evolved as the major source of information for different industry verticals like retail, marketing, advertising, tourism, hospitality, education, etc. The huge volume of data resulted in the necessity for better and efficient procedures for personalized information retrieval. Traditional data mining and information retrieval techniques based on content-based and/or collaborative filtering proved computationally costly and less scalable against the volume it must deal with. Adoption of clustering techniques is a potential solution for this problem as it can minimize the amount of data required to be managed in industrial applications like recommender systems. This empirical research focuses on evaluating multiple clustering algorithms with the goal of finding an ideal solution for clustering numerical data extracted from social media sources. Three different publicly available datasets with varying number of attributes and records from tourism domain are used for the experiments conducted as part of this work
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来源期刊
Acta Scientiarum-technology
Acta Scientiarum-technology 综合性期刊-综合性期刊
CiteScore
1.40
自引率
12.50%
发文量
60
审稿时长
6-12 weeks
期刊介绍: The journal publishes original articles in all areas of Technology, including: Engineerings, Physics, Chemistry, Mathematics, Statistics, Geosciences and Computation Sciences. To establish the public inscription of knowledge and its preservation; To publish results of research comprising ideas and new scientific suggestions; To publicize worldwide information and knowledge produced by the scientific community; To speech the process of scientific communication in Technology.
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