EDGE OSDEFINITION
K-means clustering is an unsupervised machine learning algorithm that helps in grouping data into a specific number of clusters. It generally operates by providing and assigning each data point to the nearest and specific set of cluster center a.k.a centroid. The centroids are then moved around to their optimal position. K-means is often used to identify patterns in unlabeled data however it can be affected by the scaling of your features, outliers and the correct number of clusters.
Importance of K-Means Clustering
- Ability to find patterns in data. Can identify clusters of data within unlabeled data.
- Segmentation of data. Can group large sets of data.
- Efficient method. Can process large sets of numerical data.
- Helps create business insights. Can find similarities between customer and operations behavior.
- Initial exploration of data. Can be used to explore complex sets of data.
Use Cases of K-Means Clustering
- Customer segmentation.
- Product segmentation.
- Find purchasing trends.
- Document organization.
- Operations analysis.
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