geocmeans 0.3.2.9000
Removing dependencies from rgdal and raster to move to terra. The
dataset Aracachon is now provided as a raw tiff file and must be read
directly.
Adding two new parameters to the functions CMeans, GCMeans, SFCMeans,
SFGCMeans : noise_cluster and robust. They can be used to calculate the
“robust” version of each algorithm, or to add a noise cluster. See the
vignette “Advanced examples” on the website.
Adding a little function to facilitate the scaling and unscaling of
variables : standardizer
geocmeans 0.3.1
Replacing all the functions from maptools, sp and rgeos to work now
with feature collections from sf.
geocmeans 0.2.2
removed the old function future::multiprocess, for
future::multisession as suggested in issue #3
geocmeans 0.2.1.9000
Corrected the bug in the issue #2
geocmeans 0.2.1
Minor release for correcting minor bugs and providing an updated
documentation.
geocmeans 0.2.0
New Features
- Added support to use raster data for clustering (see vignette
rasters)
- Added a S3 method to predict the membership matrix of a new set of
observations (predict.FCMres)
- Added a shiny app (function: sp_clust_explorer) for result
exploration
- The results of the functions CMeans, GCMeans,
SFCMeans, SGFCMeans are now objects of class
FCMres and the generic methods predict,
summary, plot, is and print can be
used on them. FMCres object can easily be created by hand with
results from other classifier if needed, see the new vignette
FMCres.
- Added some clustering quality indices : Negentropy Increment index,
Generalized Dunn’s index (43 and 53), David-Bouldin index,
Calinski-Harabasz index
- Added a function to perform clustering validation by bootstrap (see
function bstp_group_validation)
- Added a function to reorder the results of a classification to match
the most similar groups in a second classification
(groups_matching)
- Added functions to evaluate spatial autocorrelation of a
classification results: ELSA and FuzzyELSA (see functions
calcELSA and calcFuzzyELSA and the end of the vignette
rasters)
corrected bugs
- issue 1 fixed by editing the mapping functions. A bug occurred when
the fid of a SpatialDataFrame read from a shapefile was different from
1:nrow(df)
- an important performance gain can be observed for large dataset, the
function to compare matrices between two iterations is now significantly
faster.
- core functions rewritten with Rcpp for massive time gain
geocmeans 0.1.1