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مقارنة بعض خوارزميات التحليل العنقودي في تنقيب البيانات (Data Mining) مع واقع تطبيقي == A Comparing To Some of The Algorithms Cluster Analysis In Data Mining With Application

اسم المؤلف: محي الدين خلف ايوب

اسم المشرف: قتيبة نبيل نايف القزاز

الموضوع العام: الادارة والاقتصاد

السنة: 2015

الموضوع الدقيق: الاحصاء

الدرجة: ماجستير

الجامعة: جامعة بغداد

اللغة: العربية

مكان الجامعة: بغداد

الصفحات الاولى: 📎 07T4036 - p.pdf

المستخلص: ان التقدم العلمي المتسارع والانتشار الواسع للمعلوماتية ادى الى الاستعمال الالكتروني لمختلف المعلومات والتي اصبحت تتراكم بشكل هائل في قواعد بيانات كبيرة, وهنا تكمن اهمية البحث في محاولة تنضيج وتبويب هذا الكم الهائل من البيانات في قواعد معلومات تؤدي الغرض ا | Scientific progress is rapid and widespread Informatics web mail to various information which became accumulate dramatically, leading to try to find how tend to tab and this huge amount of data bases for information leading to the desired purpose. Work the term data mining (DM) is appropriate in this area and because of this importance of this research was to try to use data mining algorithms with the search in the accompanying circumstances. And a summary of research supports access to information and knowledge discovery through the use of techniques for data mining (DM) and also touched on the stages of exploration process of data passing through the stage of data processing and even the testing phase (F_test) to measure the case of variation or variation in the data when you reach a level of fitness (Optional). The results of the tests can be observed when changing the sample size (n) as well as the size of clusters (k) , and this leads to variation in the laboratory value (F) and in each case and her envelope. Cluster analysis of the data has spawned tests , The algorithm (K - Means) is the best , Comparing with (Single Linkage) and (Complete Linkage) algorithms A position to achieve the research hypotheses under the values shown in the tables , through calculable scale test (F_test) as well as the scale (MSE) , according to the results of experiments testing of samples sizes (n) and the size of the clusters (k) applied to the variables (v) Search.