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Stratification to Improve Systematic Sampling for Big Data Mining Using Approximate Clustering

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Machine Intelligence and Smart Systems

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Big data mining is the modern research of predicting knowledge for decision-making systems, and clustering is the data mining technique that predicts the classes of data without any prior knowledge. Big data mining supports high-volume, high-variety, and high-velocity data sets that reason classical data mining algorithms face speed-up, scale-up, memory utilization, computing efficiency, and effectiveness related problems. Data volume is the primary attribute of big data mining that is responsible for the variety and velocity related challenges. Sampling is a data reduction technique that handles data volume-related challenges under the single and multiple machine execution environments of big data mining such as classification and clustering. A good sampling process increases the speed, scalability, flexibility, accuracy, quality, efficiency, and utilized the memory resources for any data mining techniques without the influence of their characteristics. This paper proposed two sampling approaches for big data mining using the K-Means clustering algorithm known as SYK-Means (Systematic Sampling-based K-Means) and SSYK-Means (Stratified Systematic Sampling-based K-Means). The experimental results of the SYK-Means and SSYK-Means compared with classical K-Means and achieved better effectiveness and efficiency through R squares, Davies Bouldin, Calinski Harabasz, Silhouette Coefficient, CPU time, and convergence indices.

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Authors and Affiliations

  1. Department of Computer Science and Applications, Dr. Hari singh Gour Vishwavidyalaya, Sagar, India

    Kamlesh Kumar Pandey & Diwakar Shukla

Authors
  1. Kamlesh Kumar Pandey
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  2. Diwakar Shukla
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Correspondence to Kamlesh Kumar Pandey .

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Editors and Affiliations

  1. University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, Madhya Pradesh, India

    Shikha Agrawal

  2. Rustamji Institute of Technology, Gwalior, Madhya Pradesh, India

    Kamlesh Kumar Gupta

  3. King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  4. School of Information Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, Madhya Pradesh, India

    Jitendra Agrawal

  5. Vikrant Institute of Technology and Management, Gwalior, Madhya Pradesh, India

    Manish Gupta

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Pandey, K.K., Shukla, D. (2021). Stratification to Improve Systematic Sampling for Big Data Mining Using Approximate Clustering. In: Agrawal, S., Kumar Gupta, K., H. Chan, J., Agrawal, J., Gupta, M. (eds) Machine Intelligence and Smart Systems . Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4893-6_30

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