It sounds to me like you don't understand cluster analysis. You should not expect perfect "allocation" of points. I suggest that you consult references in the man pages of your functions or on the web. You might also find it useful to post on stats.stackexchange.com or a machine learning help site, as this is a statistical issue, not an R programming issue AFAICS (corrections welcome if I'm wrong about this), and so is somewhat OT here.
Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Sun, Dec 13, 2015 at 8:17 AM, Luigi Marongiu <marongiu.lu...@gmail.com> wrote: > Dear all, > I am trying to do some cluster analysis, both with the base R and the > apcluster. Both methods give 2 clusters, which is what I am looking > for since I am interested in identifying positive and negative > results. However I could not find a way to fine-tuning the analysis > in order to properly allocate the points; essentially the negative > points should be all those in the lower left portion of the plot (see > example) but some in the top centre are also given to the negative > cluster. > So how can I change the parameters to get better results? > Thank you > L > >>>> > x <- c(3.15, 3.07, 2, 3, 2.97, 45, 3.21, 45, > 40.55, 2, 22.09, 2.47, 2.97, 2.77, 2.6, 7.35, > 4.11, 37.12, 2.73, 36.36, 45, 2.33, 2.49, 45, > 2.4, 2.74, 2.64, 45, 2.47, 38.1, 2.47, 37.4, > 2.77, 2.37, 45, 2.69, 2.97, 2.7, 2, 2, 2.55, > 11.86, 2.51, 2.68, 2.31, 2.6, 2.45, 2, 2.72, > 2.57, 2.09, 3.04, 45, 45, 2.13, 43.82, 2.92, > 4.94, 24.82, 2.64, 4.96, 3.65, 2.67, 2.64, 8.04, > 4.56, 44.87, 37.42, 45, 6.2, 2.84, 4.08, 2, > 5.03, 2.27, 44.89, 2.41, 2.47, 2.78, 37.47, 45, > 2.76, 45, 2.51, 2.8, 44.8, 6.2, 2.87, 2.23, > 18.32, 3.14, 2.1, 2.38, 2.72, 2, 2, 44.41, > 3.15, 3.06, 4.8, 2.77, 2.8, 2.71, 44.77, 2.25, > 2.69, 28.38, 2, 2.95, 45, 2.79, 2.46, 2.61, > 2.78, 2.94, 38.47, 3.29, 2.89, 2.4, 2.23, 2.62, > 4.21, 2.61, 2.81, 2.41, 41.98, 2.39, 36.41, > 44.84, 4.73, 2, 2.66, 4.57, 3.01, 42.64, 2.04, > 5.49, 15.48, 3.08, 2.7, 2, 2, 2.09, 2, 2.29, > 2.92, 3.39, 3.1, 2, 6.14, 7.03, 4.77, 2.55, > 32.36, 20.61, 3.09, 4.46, 44.75, 2, 2.73, 2, > 36.05, 3.61, 34.84, 2.69, 5.28, 3.04, 45, 2.47, > 2.58, 2.16, 2.59, 45, 44.08, 2, 37.05, 2.48, > 2.46, 38.71, 7.32, 2.95, 2.8, 44.58, 42.24, > 36.99, 13.84, 45, 2, 2, 2.38, 45, 45, 43.59, > 2.69, 2.81, 3.05, 2.8, 4.65, 45, 41.46, 2.33, > 7.12, 19.18, 4.82, 4.76, 2.51, 3.1, 2.74, 4.99, > 38.06, 2.53, 2.94, 2.93, 6.59, 2.72, 2.94, 2.56, > 2.91, 44.79, 2.98, 42.95, 45, 2.63, 38.44, > 2.71, 2, 37.92, 2.69, 2.91, 2.65, 44.48, 6.35, > 2.56, 21.94, 3.08, 2.6, 45, 2, 2.62, 2.47, > 2.62, 2.73, 2.87, 2.83, 4.56, 44.22, 5.15, 5.13, > 2.76, 7.02, 28.61, 4.87, 5.02, 44.35, 2.26, > 2.89, 5.26, 38.01, 44.79, 39.26, 2.91, 4.59, > 2.69, 2.61, 34.97, 3, 45, 2.81, 2, 2.65, 2, > 37.33, 4.69, 3.26, 38.24, 4.97, 4.62, 2.47, 45, > 4.52, 2.73, 15.66, 6.06, 2.79, 2.87, 45, 45, > 45, 4.84, 3.05, 4.89, 4.64, 4.92, 2.74, 7.83, > 42.31, 2.88, 6.89, 23.06, 2.94, 4.72, 4.55, 5.52, > 4.48, 4.86, 3.12, 7.68, 43.89, 2.82, 2.64, > 3.05, 42.95, 2.33, 3.55, 45, 2.79, 2.47, 45, > 2.56, 38.33, 2.73, 2.87, 2.61, 3.01, 2.86, 2.74, > 44.46, 44.54, 2.62, 16.94, 2.53, 2.24, 2.72, 2, > 3.1, 2.88, 7.4, 4.64, 8.25, 3.01, 2.86, 2.46, > 5.67, 44.52, 2.47, 2, 29.01, 2.61, 3.23, 12.3, > 3.9, 2.91, 43.99, 36.99, 43.72, 42.29, 2.63, > 3.03, 2.85, 2.58, 2.63, 2.73, 2.57, 2.37, 2.57, > 2.75, 44.14, 39.4, 40.02, 3.08, 45, 4.96, 3, > 2.83, 2.74, 2.8, 2.8, 18.88, 4.69, 2.51, 4.32, > 2, 2.56, 2.81 > ) > y <- c(0.014, 0.04, 0.001, 0.023, 0.008, 0, 0.008, > 0.001, -0.001, 0.002, 0.103, 0, 0.013, 0.005, > 0.008, 0.001, 0.011, 0.076, 0.005, 0.045, -0.001, > 0, 0.008, -0.002, 0.002, 0.016, 0.006, 0.001, > 0.002, 0.001, 0.004, 0.086, 0.009, 0.011, 0.002, > 0.013, 0.019, 0.007, 0, 0.002, 0.024, 0.119, > 0.015, 0.009, 0.013, 0.017, 0.009, 0.009, 0.006, > 0.012, 0.002, 0.015, 0, 0.001, 0.002, 0.001, > 0.007, 0.004, 0.113, 0.016, 0.013, 0.004, 0.015, > 0.005, 0.004, 0.007, 0, 0.081, 0.001, 0.002, > 0.014, 0.002, 0, 0.01, 0.003, 0.002, 0.004, > 0.004, 0.006, 0.064, 0, 0.014, 0, 0.01, 0.019, > 0.002, 0.006, 0.005, 0.003, 0.103, 0.007, 0.008, > 0.002, 0.013, 0.007, 0.004, 0.001, 0.04, 0.017, > 0.018, 0.002, 0.006, 0.011, 0.003, 0.004, 0.008, > 0.115, 0, 0.02, 0, 0.012, 0.009, 0.011, 0.013, > 0.004, 0.058, 0.019, 0.006, 0.005, 0.004, 0.012, > 0.003, 0.003, 0.004, 0.002, 0.001, 0.002, 0.102, > -0.001, 0.008, 0.002, 0.016, 0.023, 0.014, 0.053, > 0.009, 0.001, 0.124, 0.009, 0.008, 0.002, 0.002, > 0.013, 0.002, 0.001, 0.042, 0.011, 0.009, 0, > 0.004, 0.003, 0.002, 0.005, 0, 0.101, 0.013, > 0.009, 0.005, 0.002, 0.007, 0.008, 0.067, 0.002, > 0.064, 0.028, 0.007, 0.006, 0, 0.007, 0.006, 0, > 0.001, 0.001, 0.001, 0, 0.088, 0.005, 0.008, > 0.098, 0.005, 0.019, 0.007, 0.05, -0.002, 0.002, > 0.129, 0.001, 0.004, -0.001, 0.002, -0.001, 0, > 0.043, 0.018, 0.019, 0.015, 0.003, 0.006, 0.002, > 0.001, 0.002, 0.004, 0.097, 0.025, 0.022, 0.007, > 0.011, 0.007, 0.013, 0.061, 0.008, 0.013, 0.028, > 0.004, 0.013, 0.005, 0.01, 0.004, 0, 0.006, > -0.001, 0.001, 0.01, 0.061, 0.002, 0.004, 0, > 0.011, 0.029, 0.018, 0, 0.003, 0.012, 0.085, > 0.015, 0.007, 0.002, 0.003, 0.008, 0.002, 0.007, > 0.02, 0.011, 0.02, 0.008, 0.001, 0.003, 0.01, > 0.014, 0.001, 0.096, 0.027, 0.024, 0, 0.005, > 0.006, 0.024, 0.087, 0.001, 0.083, 0.02, 0.009, > 0.009, 0.001, 0, 0.019, 0, 0.003, -0.001, 0.002, > 0, 0.089, 0.016, 0.01, 0.103, 0.003, 0.01, > 0.002, 0.008, 0.005, 0.014, 0.1, 0.007, 0.009, > 0.011, -0.001, 0, 0.002, 0.015, 0.036, 0.018, > 0.026, 0.009, 0.008, 0.004, 0.001, 0.014, 0.009, > 0.1, 0.026, 0.032, 0.008, 0.011, 0.004, 0.013, > 0.019, 0.004, 0.02, 0.015, 0.005, 0.013, -0.001, > 0.013, 0.012, 0, 0.01, 0.002, 0.001, 0.013, > 0.066, 0.009, 0.005, 0.002, 0.013, 0.025, 0.006, > 0, 0, 0.015, 0.121, 0.006, 0.003, 0.008, 0, > 0.012, 0.011, 0.003, 0.022, 0.008, 0.032, 0.007, > 0.002, 0.006, 0.007, 0, 0.003, 0.11, 0.01, 0.008, > 0, 0.018, 0.008, 0.001, 0.087, 0, 0.028, > 0.011, 0.014, 0.007, 0.001, 0.018, 0.033, 0.021, > 0.003, 0.003, 0.007, -0.001, 0.07, 0.022, 0.009, > 0.001, 0.007, 0.031, 0.008, 0.013, 0.01, 0.018, > 0.125, 0.01, 0.015, 0.006, 0, 0.015, 0.019 > ) > z <- cbind(x, y) > k <- kmeans(z, 2) > plot(z, col=k$cluster) > > library(apcluster) > m <- apclusterK(negDistMat(r=2), z, K=2, verbose=TRUE) > plot(m, z) > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the 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