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 ...

A method for the initialisation step of clustering algorithms is presented. It is based on the concept of cluster as a high density region of points. The search space is modelled as a set of d-dimensional cells. A sample of points is chosen and located into the appropriate cells. Cells are iteratively split as the number of points they receive increases. The regions of the search space having a...

We study the problem of estimating a manifold from random samples. In particular, we consider piecewise constant and piecewise linear estimators induced by k-means and k-flats, and analyze their performance. We extend previous results for k-means in two separate directions. First, we provide new results for k-means reconstruction on manifolds and, secondly, we prove reconstruction bounds for hi...

Area of classifying satellite imagery has become a challenging task in current era where there is tremendous growth in settlement i.e. construction of buildings, roads, bridges, dam etc. This paper suggests an improvised k-means and Artificial Neural Network (ANN) classifier for land-cover mapping of Eastern Himalayan state Sikkim. The improvised k-means algorithm shows satisfactory results com...