Data Stream Clustering: A Review

Number of connected devices is steadily increasing and these devices continuously generate data streams. Real-time processing of data streams is arousing interest despite many challenges. Clustering is one of the most suitable methods for real-time data stream processing, because it can be applied with less prior information about the data and it does not need labeled instances. However, data stream clustering differs from traditional clustering in many aspects and it has several challenging issues. Here, we provide information regarding the concepts and common characteristics of data streams, such as concept drift, data structures for data streams, time window models and outlier detection. We comprehensively review recent data stream clustering algorithms and analyze them in terms of the base clustering technique, computational complexity and clustering accuracy. A comparison of these algorithms is given along with still open problems. We indicate popular data stream repositories and datasets, stream processing tools and platforms. Open problems about data stream clustering are also discussed.Comment: Has been accepted for publication in Artificial Intelligence Revie

GPU hızlandırmalı ışın izleme ile radar dalga yayılımı modellemesi.

Radar producers, which are mostly in defense industry, need radar environment simulator to test their products during the development. Such a simulator helps them to be able to get rid of costly field tests. For developing a radar environment simulator, radio wave propagation should be modeled. However, this is a computationally expensive and time consuming process. Improving the performance of propagation modeling contributes to the radar development work. Ray tracing is one of the several electromagnetic wave propagation techniques. It enables calculation of total range, delay and power of radio waves on each point of the field. In this study, we have developed a radio wave propagation modeling application using a parallel implementation of the ray tracing method. Reflection, refraction and free space path loss properties of radio waves are implemented. Predefined atmosphere types that affect the refraction and surface types that affect the reflection are included for user selection. Moreover, the user has the chance of defining special atmosphere and surface types. Our application works on two-dimensional (2D) maps. It also has the ability of converting three-dimensional (3D) maps to 2D slices and working on them. We have developed and accelerated the application using GPU computing and parallel programming concepts. We have run the proposed method sequential and parallel on CPU and parallel on GPU. We have compared and analyzed time measurements of the application on different domains. We have achieved up to 18.14 speedup values between high specification CPU and GPU cards within the scope of this thesis.M.S. - Master of Scienc