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    Clustering algorithms subjected to K-mean and gaussian mixture model on multidimensional data set
    (International University of Sarajevo, 2019) Ahmed, Saadaldeen Rashid Ahmed; Al-Barazanchi, Israa; Jaaz, Zahraa; Abdulshaheed, Haider Rasheed
    This paper explored the method of clustering. Two main categories of algorithms will be used, namely k-means and Gaussian Mixture Model clustering. We will look at algorithms within thesis categories and what types of problems they solve, as well as what methods could be used to determine the number of clusters. Finally, we will test the algorithms out using sparse multidimensional data acquired from the usage of a video games sales all around the world, we categories the sales in three main standards of high sales, medium sales and low sales, showing that a simple implementation can achieve nontrivial results. The result will be presented in the form of an evaluation of there is potential for online clustering of video games sales. We will also discuss some task specific improvements and which approach is most suitable. © 2019 International University of Sarajevo.
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    Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set
    (International University of Sarajevo, 2019) Ahmed, Saadaldeen Rashid Ahmed; Al-Barazanchi, Israa; Mhana, Ammar; Abdulshaheed, Haider Rasheed
    These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about the cancer disease detection. Developing a proposed data mining model is useful to diagnose the cancer disease once the cancer detection is accomplished using data mining for the examination and classification of machine learning supervised algorithms. © 2019 International University of Sarajevo.