Reducing UK-Means to K-Means

This paper proposes an optimisation to the UK-means algorithm, which generalises the k-means algorithm to handle objects whose locations are uncertain. The location of each object is described by a probability density function (pdf). The UK-means algorithm needs to compute expected distances (EDs) between each object and the cluster representatives. The evaluation of ED from first principles is very costly operation, because the pdf’s are different and arbitrary. But UK-means needs to evaluate a lot of EDs. This is a major performance burden of the algorithm. In this paper, we derive a formula for evaluating EDs efficiently. This tremendously reduces the execution time of UK-means, as demonstrated by our preliminary experiments. We also illustrate that this optimised formula effectively reduces the UK-means problem to the traditional clustering algorithm addressed by the k-means algorithm.

• Type: Conference
• Year: 2007
• Where: ICDM
• Authors: Sau Dan Lee, Ben Kao, Reynold Cheng
• Keywords: Data Mining, ICDM 2007, K-Means Algorithm, Probability Density Function, UK-means Algorithm