Okay, some references: Samet, Hanan: "The Design and Analysis of Spatial Data Structures" and "Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS" Addison-Wesley, 1989 ISBN 0-201-50300-0 0-201-50300-X <http://www.cs.umd.edu/users/hjs/pubs.html> J. Goodman and J. O'Rourke, editors The Handbook of Discrete and Computational Geometry CRC Press LLC 1997 ISBN 0-8493-8524-5 Computational Geometry: Algorithms and Applications Mark de Berg, Marc van Kreveld, Mark Overmars, Utrecht (the Netherlands) Otfried Schwarzkopf Hong Kong (China) Springer-Verlag 1997. 377 pages ISBN: 3-540-61270-X <http://www.cs.uu.nl/geobook/index.html> Geometry in Action: Graphics <http://www.ics.uci.edu/~eppstein/gina/graphics.html> Oh, and so I can say I'm doing this in conjunction with my day job :-) "MINIMAL SPANNING TREE ANALYSIS OF FUNGAL SPORE SPATIAL PATTERNS" <http://www.swin.edu.au/chem/bio/fractals/mst01.htm> And last, but certainly not least, you MUST read the following: Near Neighbor Search in Large Metric Spaces Sergey Brin Department of Computer Science Stanford University November 20, 1995 <http://www-db.stanford.edu/~sergey/near.html> "The problem of finding the near neighbors of a given point in a large data set has been studied well and has a number of good solutions, if the data is in a simple (e.g. Euclidean), low-dimensional space. However, if the data lies in a large metric space the problem becomes much more difficult. Consider the following examples as a small sample of where this problem occurs: Information Retrieval -- Finding sentences similar to a user's query from a given database of sentences. Genetics -- Finding similar DNA or protein sequences in one of a number of large genetics databases. Speaker Recognition -- Finding similar vocal patterns (e.g., under Fourier transforms) from a database of vocal patterns. Image Recognition -- Finding images similar (using the Hausdorff metric [HKR93]) to a given one from a large image library. Video Compression -- Finding the image blocks of a previous frame that are similar to blocks in a new frame (using a simple L1 or L2 metric, possibly after a DCT transform) to generate motion vectors in MPEG video compression. Data Mining -- Finding approximate time series matches (e.g., stock histories or year long temperature)." --B Now to figure out how to implement it in MacPerl? # Fungal Parataxonomy Mycology Information (Mycoinfo) # Webmaster, Staff Writer **The World's First Mycology E-Journal** # <mailto:webmaster@mycoinfo.com> <http://www.mycoinfo.com/> # # First they ignore you. Then they laugh at you. Then they fight you. # Then you win. --Mohandas Gandhi ===== Want to unsubscribe from this list? ===== Send mail with body "unsubscribe" to macperl-request@macperl.org