You could use the clValid package to run your problem through a variety of different algorithms and evaluate cluster quality, you will learn a lot. https://cran.r-project.org/web/views/Cluster.html gives you many more options. All of the algorithms I am aware of have tunable parameters - but what is the "best" approach really depends on the detailed context of the problem domain. Sometimes dimension reduction with PCA, or even the magical tsne package is useful for preprocessing data. Finally, if you already know how your data set should be partitioned, perhaps you are really looking for a machine learning approach: see here https://cran.r-project.org/web/views/MachineLearning.html
B. On Dec 13, 2015, at 11:17 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 posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
