Mercury (planet) diagram on the right illustrates the effects of the eccentricity, showing Mercury's orbit overlaid with a circular orbit having the same semi-major axis. Mercury's higher velocity when it is near perihelion is clear from the greater distance it covers in each 5-day interval. In the diagram the varying distance of Mercury to the Sun is represented by the size of the planet, which is inversely proportional to Mercury's distance from the Sun. This varying distance to the Sun leads to Mercury's surface being flexed by tidal bulges raised by the Sun that are about 17 times stronger than the Moon's
Nearest neighbor search clustering. The database is clustered and the most "promising" clusters are retrieved. Huge gains over VA-File, tree-based indexes and sequential scan have been observed. Also note the parallels between clustering and LSH. There are numerous variants of the NNS problem and the two most well-known are the "k"-nearest neighbor search and the ε-approximate nearest neighbor search. "k"-nearest neighbor search identifies the top "k" nearest neighbors to the query. This technique is commonly used in predictive analytics to estimate or classify a point based on the consensus of its neighbors. "k"-nearest neighbor graphs are graphs in which every point is connected