A parameter-free clustering model

One way to compare clustering techniques is in terms of the part done by the computer and the part controlled by the user. This paper presents a mathematical formulation of the clustering problem in which no parameters to be controlled by the user are included, thus no outside interference is required. The model was applied to clustering data points defined in a multi-dimentional space. The experiments demonstrate that the partition depends mainly upon the structure inherent in the data set. This approach is particularly useful in the case where no preliminary information, as to the number of categories or their distribution, is available.