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
Author name:
محي الدين خلف ايوب
Supervisor name:
قتيبة نبيل نايف القزاز
General topic:
Administration and Economics
Specific topic:
Statistics
Degree:
Master
University:
University of Baghdad - Faculty Of Administration And Economics - Department Of Statistics
Language:
Arabic
University location:
Baghdad
First pages:
07T4036 - p.pdf
Abstract:
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.
