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Big data dimensionality reduction techniques in IoT: review, applications and open research challenges

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Abstract

In the age of big data, all forms of data with increasing samples and high-dimensional characteristics are demonstrating their importance in a variety of fields, including data mining, pattern recognition, machine learning, and the Internet of Things (IoTs), to name a few. The complexity of data processing increases as the dataset rises in size. The term “complexity” refers to the difficulty of finding and exploiting correlations between distinct dataset aspects. Therefore, using dimensionality reduction (DR) approach the complexity between distinct features can be eliminated. Keeping in view the betterment that can be achieved in storage and processing of big data in different IoT applications, this article reviews the literature on DR techniques with their advantages, properties, taxonomy, and parameters of evaluation. Further, the article elaborates on future research challenges, and an insight into applications of DR in different domains offers readers with information about the applicability of a certain data reduction technique.

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Rani, R., Khurana, M., Kumar, A. et al. Big data dimensionality reduction techniques in IoT: review, applications and open research challenges. Cluster Comput 25, 4027–4049 (2022). https://doi.org/10.1007/s10586-022-03634-y

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