Dear Wesley, Have a look a kmeans clustering. That will allow you to divide the data points in a given number of clusters without any other user input.
HTH, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, methodology and quality assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 thierry.onkel...@inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey -----Oorspronkelijk bericht----- Van: r-sig-geo-boun...@stat.math.ethz.ch [mailto:r-sig-geo-boun...@stat.math.ethz.ch] Namens Wesley Roberts Verzonden: maandag 11 mei 2009 14:36 Aan: Dan Putler CC: r-sig-geo@stat.math.ethz.ch Onderwerp: Re: [R-sig-Geo] Classification of attribute table Hi Dan, Thanks for the advice. I want to classify my data into three classes; canopy, non-canopy and ground based on six input variables. The input variables are mean, min, max, median, var, stdev, and kurtosis of spatially co-incident spectra associated with each segment. I have 1916 cases and the data are formatted like an ESRI attribute table, each row corresponds to one particular segment, mean min max median var stdev kurtosis 1 2 values extracted from the imagery 3 . .1916 I would thus like to classify the segments into three classes and essentially add an additional column to the attribute table with values 1, 2, and 3 denoting the class of the particular segment. Ideally the classification must be un-supervised as the whole procedure should be as automatic as possible with limited input from the user. Initially I wanted to use lda (MASS) but it required training classes. An alternative option is to use the hypothesis that segments with brighter spectra are more likely to come from tree crowns and thus just subset / select the segments which fall into for example the 90th percentile and label those as tree crowns. Many thanks, Wesley Wesley Roberts MSc. Researcher: Earth Observation (Ecosystems) Natural Resources and the Environment CSIR Tel: +27 (21) 888-2490 Fax: +27 (21) 888-2693 "To know the road ahead, ask those coming back." - Chinese proverb >>> Dan Putler <dan.put...@sauder.ubc.ca> 05/07/09 6:13 PM >>> Hi Wesley, Is this classification problem or a clustering problem? Specifically, is the ultimate goal to predict what segment a new polygon belongs in, or are you trying to form 3 segments to begin with based on the six measures you have available? If it is the latter, it is a cluster analysis problem rather than a classification problem, and you'll want to look at the Cluster Analysis and Finite Mixture Models task view at http://cran.r-project.org/web/views/Cluster.html. Dan On Thu, 2009-05-07 at 14:58 +0200, Wesley Roberts wrote: > Dear R-sig-geo users, > > I have the output of a watershed segmentation in vector format (shapefile) which has it's attribute table populated with statistics regarding spectral reflectance of each polygon object. The attribute data was sourced from a geographically co-incident aerial photograph. I would now like to classify the segments using the attribute data. This seems like an easy task but I am struggling to find a suitable method. I have looked at 'lda' and 'qda' in the MASS package but the selection of an appropriate model using 'cv1EMtrain' takes a really long time. In essence all I want to do is classify the 6 variable data set into 3 classes with the class for each case recorded in the attribute table. > > Any advice or suggestions would be greatly appreciated. > > Many thanks and kind regards, > Wesley > > > > Wesley Roberts MSc. > Researcher: Earth Observation (Ecosystems) > Natural Resources and the Environment > CSIR > Tel: +27 (21) 888-2490 > Fax: +27 (21) 888-2693 > > "To know the road ahead, ask those coming back." > - Chinese proverb > > > > -- Dan Putler Sauder School of Business University of British Columbia -- This message is subject to the CSIR's copyright terms and conditions, e-mail legal notice, and implemented Open Document Format (ODF) standard. The full disclaimer details can be found at http://www.csir.co.za/disclaimer.html. This message has been scanned for viruses and dangerous content by MailScanner, and is believed to be clean. 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