Cite Article
Cite Article
MLA
Alaziz, Sundus Naji, et al. "Clustering of COVID-19 Multi-Time Series-Based K-Means and PCA With Forecasting." IJDWM vol.19, no.3 2023: pp.1-25. http://doi.org/10.4018/IJDWM.317374
APA
Alaziz, S. N., Albayati, B., El-Bagoury, A. A., & Shafik, W. (2023). Clustering of COVID-19 Multi-Time Series-Based K-Means and PCA With Forecasting. International Journal of Data Warehousing and Mining (IJDWM), 19 (3), 1-25. http://doi.org/10.4018/IJDWM.317374
Chicago
Alaziz, Sundus Naji, et al. "Clustering of COVID-19 Multi-Time Series-Based K-Means and PCA With Forecasting," International Journal of Data Warehousing and Mining (IJDWM) 19, no.3: 1-25. http://doi.org/10.4018/IJDWM.317374
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International Journal of Data Warehousing and Mining (IJDWM) The International Journal of Data Warehousing and Mining (IJDWM) a featured IGI Global Core Journal Title, disseminates the latest international research findings in the areas of data management and analyzation. This journal is a forum for state-of-the-art developments, research, and current innovative activities focusing on the integration between the fields of data warehousing and data mining. Featured in prestigious indices including Web of Science® Citation Index Expanded®, Scopus®, Compendex®, INSPEC®, and more, this scholarly journal is led by a leading IGI Global editor and contains research from a growing list of more than 1,500+ industry-leading contributors. This journal is an ideal resource for academic researchers and practicing IT professionals looking for double-blind peer-reviewed articles that provide solutions to ongoing challenges, and new developments within this field.
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