Imports: RRF, arules, gbm, xtable, xgboost, data.table, methods
Author: Houtao Deng, Xin Guan, Vadim Khotilovich
Packaged: 2018-03-12 04:55:28 UTC; houtaodeng

person("Vadim", "Khotilovich", role = "aut"))
Description: For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code.  Reference: Interpreting tree ensembles with inTrees (Houtao Deng, 2019, <doi:10.1007/s41060-018-0144-8>).
Packaged: 2022-05-30 22:24:46 UTC; houtaodeng

Title: Initialization Algorithms for Partitioning Cluster Analysis
Authors@R: c(person("Zeynel", "Cebeci", email = "zcebeci@cukurova.edu.tr", role = c("aut", "cre")),
person("Cagatay","Cebeci", role = "aut", email = "cagataycebeci@gmail.com"))
Author: Zeynel Cebeci [aut, cre], Cagatay Cebeci [aut]
Description: Partitioning clustering algorithms divide data sets into k subsets or partitions which are so-called clusters. They require some initialization procedures for starting to partition the data sets. Initialization of cluster prototypes is one of such kind of procedures for most of the partitioning algorithms. Cluster prototypes are the data elements, i.e. centroids or medoids, representing the clusters in a data set. In order to initialize cluster prototypes, the package 'inaparc' contains a set of the functions that are the implementations of several linear time-complexity and loglinear time-complexity methods in addition to some novel techniques. Initialization of fuzzy membership degrees matrices is another important task for starting the probabilistic and possibilistic partitioning algorithms. In order to initialize membership degrees matrices required by these algorithms, a number of functions based on some traditional and novel initialization techniques are also available in the package 'inaparc'.