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K-means clustering
Definition:
K-means clustering is a popular algorithm used in unsupervised machine learning to partition a set of data points into K clusters based on their similarities and distances from cluster centroids.
The Concept of K-means Clustering in Artificial Intelligence
K-means clustering is a popular unsupervised machine learning algorithm used to group data points into clusters based on similarity. It is widely used in various fields such as data mining, image processing, and pattern recognition.
How Does K-means Clustering Work?
K-means clustering works by partitioning a dataset into 'k' clusters where each data point belongs to the cluster with the nearest mean. The algorithm iteratively assigns data points to the nearest cluster based on the mean of the cluster and then recomputes the centroids of the clusters until convergence is reached.
Applications of K-means Clustering
K-means clustering is commonly used in customer segmentation, anomaly detection, and pattern recognition. In customer segmentation, businesses use K-means clustering to group customers with similar behaviors or preferences for targeted marketing strategies. In anomaly detection, the algorithm can be used to identify outliers in a dataset that do not fit any cluster. Additionally, in pattern recognition, K-means clustering can help classify data points into distinct categories.
Advantages and Limitations of K-means Clustering
Advantages:
- Simple and easy to implement
- Efficient for large datasets
- Applicable to a wide range of domains
Limitations:
- Requires the number of clusters 'k' to be predefined
- Sensitive to outliers
- May converge to local optima depending on the initial centroids
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