Big Data Analytics: A Tutorial of Some Clustering Techniques
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...
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IJMADA
2021-09-01
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| Series: | International Journal of Management and Data Analytics |
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| Online Access: | https://ijmada.com/index.php/ijmada/article/view/8 |
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| author | Said Baadel |
| author_facet | Said Baadel |
| author_sort | Said Baadel |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fa70f5dc70ce4453b1ad2770a2f742a2 |
| institution | Kabale University |
| issn | 2816-9395 |
| language | English |
| publishDate | 2021-09-01 |
| publisher | IJMADA |
| record_format | Article |
| series | International Journal of Management and Data Analytics |
| spelling | doaj-art-fa70f5dc70ce4453b1ad2770a2f742a22025-01-20T15:45:31ZengIJMADAInternational Journal of Management and Data Analytics2816-93952021-09-01384610.5281/zenodo.115274468Big Data Analytics: A Tutorial of Some Clustering TechniquesSaid BaadelData 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.https://ijmada.com/index.php/ijmada/article/view/8big dataclusteringk-meansk-medoidmcokeoverlapping clustering |
| spellingShingle | Said Baadel Big Data Analytics: A Tutorial of Some Clustering Techniques International Journal of Management and Data Analytics big data clustering k-means k-medoid mcoke overlapping clustering |
| title | Big Data Analytics: A Tutorial of Some Clustering Techniques |
| title_full | Big Data Analytics: A Tutorial of Some Clustering Techniques |
| title_fullStr | Big Data Analytics: A Tutorial of Some Clustering Techniques |
| title_full_unstemmed | Big Data Analytics: A Tutorial of Some Clustering Techniques |
| title_short | Big Data Analytics: A Tutorial of Some Clustering Techniques |
| title_sort | big data analytics a tutorial of some clustering techniques |
| topic | big data clustering k-means k-medoid mcoke overlapping clustering |
| url | https://ijmada.com/index.php/ijmada/article/view/8 |
| work_keys_str_mv | AT saidbaadel bigdataanalyticsatutorialofsomeclusteringtechniques |