The most widely used partitioning method in cluster analysis is the k-means clustering which minimizes within-cluster sum of squares. However, the k-means clustering is sensitive to outliers or cluster structures. We introduce the k-spatial medians clustering which is less sensitive to outliers as an alternative to the k-means clustering and compare two clustering methods for some arti cial dat...

A new clustering algorithm based on grid projections is proposed. This algorithm, called U*C, uses distance information together with density structures. The number of clusters is determined automatically. The validity of the clusters found can be judged by the U*-Matrix visualization on top of the grid. A U*-Matrix gives a combined visualization of distance and density structures of a high dim...

k-means is the basic method applied in many data clustering problems. As is known, its natural modification can be applied to projection clustering by changing the cost function from the squared-distance from the point to the squared distance from the affine subspace. However, to apply thus approach we need the beforehand knowledge of the dimension. In this paper we show how to modify this appr...

Clustering is used to organize data for efficient retrieval. One of the problems in clustering is the identification of clusters in given data. A popular technique for clustering is based on K-means such that the data is partitioned into K clusters. In this method, the number of clusters is predefined and the technique is highly dependent on the initial identification of elements that represent...

This paper investigates how performance of students progresses during their studies. Progression of a student is defined as a tuple that shows how a year average stays the same, increases or decreases compared to first year. Taking the data of two consecutive cohorts and using k-means clustering, five meaningful types of progressions are put in evidence and intuitively visualized with a deviati...

Wireless sensor networks consist of sensor nodes with limited computational and communication capabilities. In this paper, the whole network of sensor nodes is divided into clusters based on their physical locations. In addition, efficient ways of key distribution among the nodes within the cluster and among controllers of each cluster are discussed. Also, inter and intra cluster communications...

Özet. Yazılım efor tahmini, yazılım proje yönetiminde çok önemli bir aşamadır. Tahmin değerinin doğruluğu proje başarı ya da başarısızlığına doğrudan etki eder. Yöneticiler uygun kaynakları tahmin etmeye çalışırlar ve bu yönetim için zorlayıcı bir durumdur. Araç ve tekniklerin yardımıyla tahmin süreci daha iyi gerçekleştirilebilir. COCOMO en çok kullanılan, parametrik modellerden biri olarak if...

Recently, [3] introduced an SDP relaxation of the k-means problem in R. In this work, we consider a random model for the data points in which k balls of unit radius are deterministically distributed throughout R, and then in each ball, n points are drawn according to a common rotationally invariant probability distribution. For any fixed ball configuration and probability distribution, we prove...

K-means clustering algorithm has been used to classify patterns of Japanese candlesticks which accompany the prices of several assets registered in the Warsaw stock exchange (GPW). It has been found that the trend reversals seem to be preceded by specific combinations of candlesticks with notable frequency. Surprisingly, the same patterns appear in both bullish and bearish trend reversals. The ...