Using mahout library for clustering algorithm a case study on healthcare data| International Journal of Innovative Science and Research Technology

Using Mahout Library for Clustering Algorithm: A Case Study on Healthcare Data

Authors : Dr. Divya Chauhan; Dr. Satpal

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/4y9pz4nd

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DOI : https://doi.org/10.38124/ijisrt/25nov1348

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Abstract : Data mining techniques and algorithms worked excellently with small datasets. Data mining algorithms analysed bulk data to identify trends and draw conclusions. But most data mining tool is not efficient to process very large dataset which is the case in big data. They are not able to give quick outcomes in quick time, unless the computational tasks are run on multiple machines distributed over cloud. For process large volume of data like big data, Hadoop has adopted a new set of library for machine learning called Mahout. This paper deals with the clustering algorithms with the help of mahout library in Hadoop MapReduce environment. The real-world healthcare dataset is used which is quite large in size. The three clustering algorithms used are canopy clustering, K-Means clustering and fuzzy K-Means clustering.

Keywords : Data Mining, Big Data, Hadoop, Mahout, Clustering, Healthcare.

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Data mining techniques and algorithms worked excellently with small datasets. Data mining algorithms analysed bulk data to identify trends and draw conclusions. But most data mining tool is not efficient to process very large dataset which is the case in big data. They are not able to give quick outcomes in quick time, unless the computational tasks are run on multiple machines distributed over cloud. For process large volume of data like big data, Hadoop has adopted a new set of library for machine learning called Mahout. This paper deals with the clustering algorithms with the help of mahout library in Hadoop MapReduce environment. The real-world healthcare dataset is used which is quite large in size. The three clustering algorithms used are canopy clustering, K-Means clustering and fuzzy K-Means clustering.

Keywords : Data Mining, Big Data, Hadoop, Mahout, Clustering, Healthcare.