A random sample of size N is divided into k clusters that minimize the within cluster sum of squares locally. This k-means clustering method can be used as a quick procedure for constructing variable-cell historgrams that have no empty cell. A histogram estimate is proposed in this paper, and is shown to be uniformly consistent in probability.

We discuss a beer recommendation engine that predicts whether a user has had a given beer as well as the rating the user will assign that beer based on the beers the user has had and the assigned ratings. k-means clustering is used to group similar users for both prediction problems. This framework may be valuable to bars or breweries trying to learn the preferences of their demographic, to con...

This paper proposes a distributed PCA algorithm, with the theoretical guarantee that any good approximation solution on the projected data for k-means clustering is also a good approximation on the original data, while the projected dimension required is independent of the original dimension. When combined with the distributed coreset-based clustering approach in [3], this leads to an algorithm...

In Forensic Analysis thousands of files are usually examined. Data in those files consists of unstructured text analyzing it by examiners is very difficult. Algorithms for clustering documents can facilitate the discovery of new and useful knowledge from the documents under analysis. Cluster analysis itself is not one specific algorithm but the general task to be solved. It can be achieved by v...

Clustering is used to identify the relationship among different objects from large volume of data. The clustering analysis is feasible only when the groups are formed with important features. The existing K-Means clustering processing time and the computation cost is high. The proposed two level variable weighting algorithm calculates weights for both views and variables to identify the importa...

This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting.This paper proposes a new algorithm for K-medoids clustering which runs like the. A new Kmedoids clustering method that should be fast and efficient.

While the mobile game industry is growing with each passing day with the popularization of 3G smart devices, the creation of successful games, which may interest users, become quite important in terms of the survival of the designed game. Clustering, which has many application fields, is a successful method and its implementation in the field of mobile games is inevitable. In this study, classi...

Figure 15.1: k=3 clusters with red points chosen as facilities. Consider a situation where we have n point locations and we wish to place k facilities among these points to provide some service. It is desirable to have these facilities close to the points they are serving, but the notion of “close” can have different interpretations. The k-means problem seeks to place k facilities so as to mini...

◮ It can compute exact and approximate probabilities with error guarantees for the clustering output. State-of-the-art techniques (e.g. UK-means, UKmedoids, MMVar): ◮ do not support the possible worlds semantics, ◮ lack support for correlations and assume probabilistic independence, ◮ use deterministic cluster medoids or expected means, and ◮ can only compute clustering based on expected distan...