Journal of Statistical Software
Công bố khoa học tiêu biểu
Sắp xếp:
<b>bridgesampling</b>: An <i>R</i> Package for Estimating Normalizing Constants
Journal of Statistical Software - Tập 92 Số 10
MCMC Methods for Multi-Response Generalized Linear Mixed Models: The<b>MCMCglmm</b><i>R</i>Package
Journal of Statistical Software - Tập 33 Số 2
<b>ranger</b>: A Fast Implementation of Random Forests for High Dimensional Data in <i>C++</i> and <i>R</i>
Journal of Statistical Software - Tập 77 Số 1
<b>CircStat</b>: A<i>MATLAB</i>Toolbox for Circular Statistics
Journal of Statistical Software - Tập 31 Số 10
Model-based Methods of Classification: Using the<b>mclust</b>Software in Chemometrics
Journal of Statistical Software - Tập 18 Số 6
<b>DiceDesign</b>and<b>DiceEval</b>: Two<i>R</i>Packages for Design and Analysis of Computer Experiments
Journal of Statistical Software - Tập 65 Số 11
<b>DiceKriging</b>,<b>DiceOptim</b>: Two<i>R</i>Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization
Journal of Statistical Software - Tập 51 Số 1
NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set
Journal of Statistical Software - Tập 61 - Trang 1 - 36 - 2014
Clustering is the partitioning of a set of objects into groups (clusters) so that objects within a group are more similar to each others than objects in different groups. Most of the clustering algorithms depend on some assumptions in order to define the subgroups present in a data set. As a consequence, the resulting clustering scheme requires some sort of evaluation as regards its validity.
The evaluation procedure has to tackle difficult problems such as the quality of clusters, the degree with which a clustering scheme fits a specific data set and the optimal number of clusters in a partitioning. In the literature, a wide variety of indices have been proposed to find the optimal number of clusters in a partitioning of a data set during the clustering process. However, for most of indices proposed in the literature, programs are unavailable to test these indices and compare them.
The R package NbClust has been developed for that purpose. It provides 30 indices which determine the number of clusters in a data set and it offers also the best clustering scheme from different results to the user. In addition, it provides a function to perform k-means and hierarchical clustering with different distance measures and aggregation methods. Any combination of validation indices and clustering methods can be requested in a single function call. This enables the user to simultaneously evaluate several clustering schemes while varying the number of clusters, to help determining the most appropriate number of clusters for the data set of interest.
Conducting Meta-Analyses in<i>R</i>with the<b>metafor</b>Package
Journal of Statistical Software - Tập 36 Số 3
Tổng số: 28
- 1
- 2
- 3