A Review of Data and Document Clustering pertaining to various Distance Measures

Abstract

Data is being generated at an increasing rate in a variety of fields as science and technology advance. The generated data are being saved for future decision-making. Data mining is the process of extracting patterns and useful information from massive amounts of data. The distance measure, which is used to calculate how different two objects are from one another, is one such instrument. We have conducted a comprehensive survey of how the distance measures behave when employed with different algorithms. Furthermore, the effectiveness and performance of some novel similarity measures proposed by other authors are investigated.

Random and systematic errors in pairwise computer programming: A systematic review

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The contribution of rural social networks to the consolidation of social capital

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Predicting Student Dropout based on Machine Learning and Deep Learning: A Systematic Review

“…This issue entails economic, social, and educational consequences for the stakeholders in the global education system, ranging from the psychological impact on students to the management challenges faced by government entities 4,5 . To address the problem, predicting and managing early signs of student dropout is relevant [6][7][8][9] . This will enable educational institutions to act promptly, implementing preventive and proactive measures to address the issue and reduce the dropout rate 10 .…”