Fast and scalable data mining algorithms for clustering using graphics processors - Salli Aruliah Arul Shalom. (2014)
The tremendous growth of data in the order of zeta bytes in various real-life databases has fortified the need for high speed and scalable data mining methods. Computing applications for data warehouses and data streams are highly arithmetic and computationally intensive, thus demanding high performance data processing methods. Clustering techniques at large form the foundational building blocks in data mining, machine learning, etc. In light of this aspiration, researchers have persevered vigorously to design and develop efficient schemes for different data mining tasks. Clustering is an important data mining task. In this thesis our focus is on clustering.