Data clustering is a process of arranging similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is better than among groups. In this paper a hybrid clustering algorithm based on K-mean and K-harmonic mean (KHM) is described. The proposed algorithm is tested on five different datasets. The research is focused on fast an...

В настоящее время происходит активное накопление данных большого объёма в различных информационных средах, таких как социальные, корпоративные, научные и другие. Интенсивное использование больших данных в различных областях стимулирует повышенный интерес исследователей к развитию методов и средств обработки и анализа массивных данных огромных объёмов и значительного многообразия. Одним из персп...

Software projects are usually analyzed by experts based on their previous experience, their intuition and data they gather about the project. In this work, we show an approach for a purely data-driven retrospective project analysis. We plan to build on this work to make predictions about the evolution of software projects.

K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights...

We develop the heuristic PROBI for the probabilistic Euclidean k-median problem based on a coreset construction by Lammersen et al. [28]. Our algorithm computes a summary of the data and then uses an adapted version of k-means++ [5] to compute a good solution on the summary. The summary is maintained in a data stream, so PROBI can be used in a data stream setting on very large data sets. We exp...

Hierarchical clustering methods have attracted much attention by giving the user a maximum amount of flexibility. Rather than requiring parameter choices to be predetermined, the result represents all possible levels of granularity. In this paper a hierarchical method is introduced that is fundamentally related to partitioning methods, such as k-medoids and k-means as well as to a density based...

Özet. Yazılım teknolojileri hızla ilerlemekte ve buna paralel olarak hem kamu alanında hem de özel sektörde gerçekleştirilen yazılım projelerinin sayısı artmaktadır. Yazılım otomasyon projelerinden elde edilen en büyük çıktılardan birisi kuşkusuz ki üretilen verilerdir. Yüksek boyutlu, anlaşılması güç bu verilerin işlenerek, daha anlamlı ve yönlendirici verilere dönüştürülmesi önemli bir ihtiya...

Cloud is the major obstacle to analyze data in the satellite images. The various approaches are used to remove the cloud from the satellite image for further processing. The approaches are in-painting and multi-temporal. But, the algorithm working for these approaches cannot produce the accurate results. So, that the accuracy assessment helps to motivate the increased accuracy result. The main ...

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...