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.

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...

In this paper, we describe our work at subtopic mining subtask in NTCIR-9 in simplified Chinese. To find possible subtopics of a specific query, we select related queries recorded by query log, or titles of searching results provided by Google and Baidu, or the catalog of corresponding entry in Baidu encyclopedia, which are lexically similar as the original query, then we apply k-means algorith...

This paper reports a comparison of calculated molecular properties and of 2D fragment bit-strings when used for the selection of structurally diverse subsets of a file of 44295 compounds. MaxMin dissimilarity-based selection and k-means clusterbased selection are used to select subsets containing between 1% and 20% of the file. Investigation of the numbers of bioactive molecules in the selected...

Minkowski Weighted K-Means is a variant of K-Means set in the Minkowski space, automatically computing weights for features at each cluster. As a variant of K-Means, its accuracy heavily depends on the initial centroids fed to it. In this paper we discuss our experiments comparing six initializations, random and five other initializations in the Minkowski space, in terms of their accuracy, proc...

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Utilizing the sample size of a dataset, the random cluster model is employed in order to derive an estimate of the mean number of K-Means clusters to form during classification of a dataset.

Due to the progressive growth of the amount of data available in a wide variety of scientific fields, it has become more difficult to manipulate and analyze such information. Even though datasets have grown in size, the K-means algorithm remains as one of the most popular clustering methods, in spite of its dependency on the initial settings and high computational cost, especially in terms of d...