Dynamic Clustering

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Revision as of 11:22, 26 April 2009 by Nat (talk | contribs) (rewrite, hope someone can write better than me)
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Dynamic clustering is a technique to find similar item in your log. The ideas is to put a "state" for each item in your log. The state can contain any data that you seem valuable, such as lateral velocity or distance. Save this along with your data. When you need a data, consider "cluster" them. The easiest way doing this to to find a "distance" between current state and past states. Distance can be Euclidian (sqrt(sqr(dist1 - dist2) + sqr(lat1 - lat2))) or another way, such as Manhatton distance. Took N states with the lower distance, that's your data.

Earlier method doing this linearly by iterate through the log and calculate distance for each one respectively. If you have more than 100,000 states in your log, it will heavily slow. The new approach is K-d Tree. Corbos was the first one who mentioning this on the wiki, and get catch in interest by Chase-san and Simonton. As of now, Simonton's K-d Tree is one of fastest tree.

Bot using this technique

See Also