a well-known algorithm of clustering is k-means by which the data are divided into k classes based upon a distance criterion. in the present research, by using k-means method for classifying data derived from exploration boreholes in the parkam deposit, the optimum k has been calculated and then the data have been clustered and the relative geochemical behavioral characteristics analyzed. the c...

In this work, we study the k-means cost function. The (Euclidean) k-means problem can be described as follows: given a dataset X ⊆ R and a positive integer k, find a set of k centers C ⊆ R such that Φ(C,X) def = ∑ x∈X minc∈C ||x− c|| 2 is minimized. Let ∆k(X) def = minC⊆Rd Φ(C,X) denote the cost of the optimal k-means solution. It is simple to observe that for any dataset X, ∆k(X) decreases as ...

This paper reflects the results of an implementation of the K-means algorithm on U.N survey data on people’s priorities, organized by country. The dataset includes 16 features for each country, with each feature corresponding to a different societal issue. Each country has a rating in the range of [0, 1] that indicates how important a particular feature or issue is to that country’s people– the...

Landslide databases and input parameters used for modeling landslide hazard often contain imprecisions and uncertainties inherent in the decision-making process. Dealing with imprecision and uncertainty requires techniques that go beyond classical logic. In this paper, methods of fuzzy k -means classification were used to assign digital terrain attributes to continuous landform classes whereas ...