Big Data Analytics: A Tutorial of Some Clustering Techniques

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Said Baadel


Data Clustering or unsupervised classification is one of the main research areas in Data Mining. Partitioning Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard (crisp) partitioning techniques where each object is assigned to one cluster. The most widely used in hard partitioning algorithm is the K-means and its variations and extensions such as the K-Medoid. Other algorithms use overlapping techniques where an object may belong to one or more clusters. Partitioning algorithms that overlap include the commonly used Fuzzy K-means and its variations. Other more recent algorithms reviewed in this paper are the Overlapping K-Means (OKM), Weighted OKM (WOKM) the Overlapping Partitioning Cluster (OPC) and the Multi-Cluster Overlapping K-means Extension (MCOKE). This tutorial focuses on the above-mentioned partitioning algorithms. We hope this paper can be beneficial to students, educational institutions, and any other curious mind trying to learn and understand the k-means clustering algorithm.


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Big Data Analytics: A Tutorial of Some Clustering Techniques. (2021). International Journal of Management and Data Analytics, 1(2), 38-46.
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How to Cite

Big Data Analytics: A Tutorial of Some Clustering Techniques. (2021). International Journal of Management and Data Analytics, 1(2), 38-46.


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