The 2014 KDD conference gave a test of time award to a 1996 paper by Kriegel et al that proposed an algorithm for accomplishing this task which improved upon the CLARANS algorithm to work against data sets of arbitrary shapes.
The 2014 KDD conference gave a test of time award to a 1996 paper by Kriegel et al that proposed an algorithm for accomplishing this task which improved upon the CLARANS algorithm to work against data sets of arbitrary shapes. The branching factor is a parameter to an algorithm that performs this task and generates a Characteristic Feature Tree as implemented in the BIRCH module for Python's scikit-learn library. A noise-resistant algorithm to perform this task is called DBSCAN. One algorithm for accomplishing this task is similar to Lloyd's algorithm (*) and attempts to minimize the WCSS within the namesake number of inputs. A popular way of doing this task is to iteratively update membership using distance to the nearest representative centroid as the criterion, then updating the centroid; that algorithm is called k-means. These types of algorithms are unsupervised, unlike algorithms for the related problem of classification.  For 10 points, name this type of algorithm used in machine learning to put similar items into groups.
The 2014 KDD conference gave a test of time award to a 1996 paper by Kriegel et al that proposed an algorithm for accomplishing this task which improved upon the CLARANS algorithm to work against data sets of arbitrary shapes.
clustering algorithm [or cluster analysis algorithms]