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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IJMADA
2021-09-01
|
| Series: | International Journal of Management and Data Analytics |
| Subjects: | |
| Online Access: | https://ijmada.com/index.php/ijmada/article/view/8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2816-9395 |