Techniques K means agglomerative etc

In data mining and warehousing, various techniques are used to analyze and cluster large datasets. Two commonly used techniques for clustering are K-means and agglomerative clustering.

K-means clustering is a partition-based technique that separates data points into k clusters based on their similarity. The algorithm initializes the process by randomly selecting k centroids and then iteratively assigns each data point to the nearest centroid, calculates the mean of the assigned points, and updates the centroids. This process continues until the centroids no longer change, indicating convergence. K-means is widely used in market segmentation, image processing, and document clustering.

Agglomerative clustering, on the other hand, is a hierarchical technique that starts with individual data points and merges them into successively larger clusters based on their distance or similarity. The algorithm starts with each data point as a separate cluster and then merges the closest pair of clusters at each iteration, until all the data points belong to a single cluster. Agglomerative clustering is used in image segmentation, social network analysis, and bioinformatics.

Both techniques have their advantages and disadvantages. K-means is faster and more scalable, but it requires the number of clusters to be specified in advance, and it can be sensitive to the initial placement of centroids. Agglomerative clustering is slower but more flexible and does not require the number of clusters to be predetermined. However, it is computationally expensive and can be sensitive to the choice of distance measure and linkage criteria.