Importance

Loading Importance Indicators...

Abstract

As an optimization problem, clustering data (unsupervised learning) is known to be a difficult problem. Most practical approaches use a heuristic, typically gradient-descent, algorithm to search for a solution in the huge space of possible solutions. Such methods are by definition sensitive to starting points. It has been well-known that clustering algorithms are extremely sensitive to initial conditions. Most methods for guessing an initial solution simply make random guesses. In this paper we present a method that takes an initial condition and efficiently produces a refined starting condition. The method is applicable to a wide class of clustering algorithms for discrete and continuous data. In this paper we demonstrate how this method is applied to the popular K-means clustering algorithm and show that refined initial starting points indeed lead to improved solutions. The technique can be used as an initializer for other clustering solutions. The method is based on an efficient technique for estimating the modes of a distribution and runs in time guaranteed to be less than overall clustering time for large data sets. The method is also scalable and hence can be efficiently used on huge databases to refine starting points for scalable clustering algorithms in data mining applications.

Loading the Abstract Image...

First Claim