K Means Clustering - HANA Predictive Analysis Library - What is it?

In SAP HANA predictive analysis, k-means clustering is a method of cluster analysis. The k-means algorithm partitions n observations or records into k clusters in which each observation belongs to the cluster with the nearest center. In marketing and customer relationship management areas, this algorithm uses customer data to track customer behavior and create strategic business initiatives. Organizations can thus divide their customers into segments based on variants such as demography, customer behavior, customer profitability, measure of risk, and lifetime value of a customer or retention probability.

k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.

Clustering works to group records together according to an algorithm or mathematical formula that attempts to find centroids, or centers, around which similar records gravitate. The most common algorithm uses an iterative refinement technique. It is also referred to as Lloyd's algorithm















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