See the clara() function from the cluster package. It scales fairly well to larger-sizes data sets.
Cheers, Dylan On Mon, May 11, 2009 at 5:35 AM, Wesley Roberts <[email protected]> wrote: > 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 <[email protected]> 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. MailScanner thanks Transtec Computers for their > support. > > _______________________________________________ > R-sig-Geo mailing list > [email protected] > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
