Using the Triangle Inequality to Accelerate k-Means
Source: University of California
The k-means algorithm is by far the most widely used method for discovering clusters in data. The paper shows how to accelerate it dramatically, while still always computing exactly the same result as the standard algorithm. The accelerated algorithm avoids unnecessary distance calculations by applying the triangle inequality in two different ways, and by keeping track of lower and upper bounds for distances between points and centers. Experiments show that the new algorithm is effective for datasets with up to 1000 dimensions, and becomes more and more effective as the number of clusters increases. For it is many times faster than the best previously known accelerated k-means method.