Evaluating and comparing between fuzzy K-mean and K-mean clustering in general cloud

العناوين الأخرى

تقييم و مقارنة خوارزمية تجميع البيانات الضبابية الوسطية و خوارزمية تجميع البيانات الوسطية في البيئة السحابية

مقدم أطروحة جامعية

al-Dumur, Mahmud Awad Muslim

مشرف أطروحة جامعية

al-Abbadi, Muhammad Ali Husayn
al-Tarawinah, Raghad Muhammad

أعضاء اللجنة

al-Abadilah, Ahmad Hamad Hammud
al-Kasasibah, Muhammad Sharari Zamil
Sulayman, Hamzah Sabah Iyal

الجامعة

جامعة مؤتة

الكلية

كلية تكنولوجيا المعلومات

القسم الأكاديمي

قسم الحاسوب

دولة الجامعة

الأردن

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2017

الملخص الإنجليزي

As the popularity of cloud computing has grown and with the continuous cheapening of computational resources there is a current trend of migration of services to cloud providers, capable of processing large amounts of data (large data) ,So the clustering in the cloud environments become a real process.

Clustering is a form of data modeling which is based on the construction of clusters.

Clusters are data sets that display the Property: the elements belonging to the same set that elements belonging to any other set, in a certain criterion of similarity.

Grouping of data in a cloud can be achieved using many different clustering techniques, this research attempts to evaluate the two clustering algorithms: Fuzzy K-mean data clustering algorithm (FKM) and K-mean data clustering algorithm (KM) in the cloud environment by adding them to the cloud computing simulator, to analyze the effect of some change factors on them, and finally comparing the results of the two added algorithms with each other and with the existing clustering algorithm in the simulator environment (vertical clustering algorithm VC & horizontal clustering algorithm HC).

The comparison factors used in the experiment are: the number of the data points to be clustered (large or small), the number of cluster and the type of dataset (random or ideal).

A cloud computing simulator that called work flowsim was used in the experiments that evaluate the algorithm’s behavior in different usage conditions.

FKM clustering algorithm gave close results to KM clustering algorithm, but it still requires more execution time than K-Means clustering, and the vertical clustering algorithm has the most execution time between all of the clustering algorithms (FKM, K-mean and HC) in all of the comparison cases.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

عدد الصفحات

45

قائمة المحتويات

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-780523