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{ "item_title" : "Big Data Mining and Analytics with Applications", "item_author" : [" Hongzhi Wang", "Chen Ye "], "item_description" : "This Reprint contains all of the articles accepted and published in the Special Issue titled Big Data Mining and Analytics with Applications in the journal Mathematics. It compiles 10 papers related to span predictive modeling for spatiotemporal data (e.g., traffic, air quality, crowd flow), data quality and repair, automated data understanding (e.g., table type prediction), advanced clustering and user segmentation, scalable statistical learning (e.g., Bayesian variable selection), and text mining applications (e.g., document classification, fake review detection). Together, they highlight a strong focus on efficient, robust, and interpretable methods for big data mining and analysis across domains. It is our hope that these contributions will serve as an inspiring source for researchers in the domains of big data analytics and machine learning.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/72/586/336/3725863369_b.jpg", "price_data" : { "retail_price" : "83.71", "online_price" : "83.71", "our_price" : "83.71", "club_price" : "83.71", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Big Data Mining and Analytics with Applications|Hongzhi Wang

Big Data Mining and Analytics with Applications

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Overview

This Reprint contains all of the articles accepted and published in the Special Issue titled "Big Data Mining and Analytics with Applications" in the journal Mathematics. It compiles 10 papers related to span predictive modeling for spatiotemporal data (e.g., traffic, air quality, crowd flow), data quality and repair, automated data understanding (e.g., table type prediction), advanced clustering and user segmentation, scalable statistical learning (e.g., Bayesian variable selection), and text mining applications (e.g., document classification, fake review detection). Together, they highlight a strong focus on efficient, robust, and interpretable methods for big data mining and analysis across domains. It is our hope that these contributions will serve as an inspiring source for researchers in the domains of big data analytics and machine learning.

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Details

  • ISBN-13: 9783725863365
  • ISBN-10: 3725863369
  • Publisher: Mdpi AG
  • Publish Date: January 2026
  • Dimensions: 9.61 x 6.69 x 0.69 inches
  • Shipping Weight: 1.38 pounds
  • Page Count: 196

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