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K-means is an essential algorithm for Data Science. But it's confusing for beginners. Let me demolish your confusion:
1. K-Means K-means is a popular unsupervised machine learning algorithm used for clustering. It's a core algorithm used for customer segmentation, inventory categorization, market segmentation, and even anomaly detection.
2. Unsupervised: K-means is an unsupervised algorithm used on data with no labels or predefined outcomes. The goal is not to predict a target output, but to explore the structure of the data by identifying patterns, clusters, or relationships within the dataset.
3. Objective Function: The objective of K-means is to minimize the within-cluster sum of squares (WCSS). It does this though a series of iterative steps that include Assignments and Updated Steps.
4. Assignment Step: In this step, each data point is assigned to the nearest cluster centroid. The "nearest" is typically determined using the Euclidean distance.
5. Update Step: Recalculate the centroids as the mean of all points in the cluster. Each centroid is the average of the points in its cluster.
6. Iterate Step(s): The assignment and update steps are repeated until the centroids no longer change significantly, indicating that the clusters are as good as stable. This process minimizes the within-cluster variance.
7. Silhouette Score (Evaluation): This metric measures how similar a data point is to its own cluster compared to other clusters. The silhouette score ranges from -1 to 1, where a high value indicates that the data point is well-matched to its own cluster and poorly matched to neighboring clusters.
8. Elbow Method (Evaluation): This method involves plotting the inertia as a function of the number of clusters and looking for an 'elbow' in the graph. The elbow point, where the rate of decrease sharply changes, can be a good choice for the number of clusters.
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