This paper evaluates the use of the fuzzy k-means clustering method for the clustering of files of 2D chemical structures. Simulated property prediction experiments with the Starlist file of logP values demonstrate that use of the fuzzy k-means method can, in some cases, yield results that are superior to those obtained with the conventional k-means method and with Ward's clustering method. Clu...

The issue of determining “the right number of clusters” in K-Means has attracted considerable interest, especially in the recent years. Cluster intermix appears to be a factor most affecting the clustering results. This paper proposes an experimental setting for comparison of different approaches at data generated from Gaussian clusters with the controlled parameters of betweenand within-cluste...

For the peak picking of tempo candidates, applying kmeans clustering on tempo curve is straightforward and leading to good result. But the tempo candidates obtained from tempo curve are limited and lose a lot of information for possible tempi. The study proposes the local maximum peak picking method to increase the number and information of possible tempo candidates. Therefore, the accuracy of ...

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.

Most partition-based cluster analysis methods (e.g., kmeans) will partition any dataset D into k subsets, regardless of the inherent appropriateness of such a partitioning. This paper presents a family of permutation-based procedures to determine both the number of clusters k best supported by the available data and the weight of evidence in support of this clustering. These procedures use one ...

As we know, kmeans method is a very effective algorithm of clustering. Its most powerful feature is the scalability and simplicity. However, the most disadvantage is that we must know the number of clusters in the first place, which is usually a difficult problem in practice. In this paper, we propose a new approach– peak-searching clustering– to realize clustering without given the number of c...

Determining the number of clusters present in a dataset is an important problem cluster analysis. Conventional clustering techniques generally assume this parameter to be provided up front. %user supplied. %Recently, robustness any given algorithm analyzed measure stability/instability which turn determines number. In paper, we propose method analyzes stability for predicting Under same computa...

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...