Big Data Analytics: A Tutorial of Some Clustering Techniques

Main Article Content

Said Baadel

Abstract


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.



 

Article Details

How to Cite
Big Data Analytics: A Tutorial of Some Clustering Techniques. (2021). International Journal of Management and Data Analytics, 1(2), 38-46. https://doi.org/10.5281/zenodo.11527446
Section
Regular Paper

How to Cite

Big Data Analytics: A Tutorial of Some Clustering Techniques. (2021). International Journal of Management and Data Analytics, 1(2), 38-46. https://doi.org/10.5281/zenodo.11527446

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