It addresses drawbacks of recent algorithms in discovering clusters of arbitrary densities. In this thesis, a new grid-based and density-based algorithm is proposed for clustering data streams. An offline component is then used to cluster the grid cells based on density information. Relative density relatedness measures and a dynamic range neighborhood are proposed to differentiate clusters of arbitrary densities. The huge size of a continuously flowing data has put forward a number of challenges in data stream analysis. However, current clustering algorithms still lack the ability to efficiently discover clusters of arbitrary densities in data streams. Exploration of the structure of streamed data represented a major challenge that resulted in introducing various clustering algorithms. The experimental evaluation shows considerable improvements upon the state-of-the-art algorithms in both clustering... The algorithm uses an online component to map the input data to grid cells.
A concise and gripping account of eugenics from its origins in the twentieth century and beyond.