| Title: | MST-kNN Clustering Algorithm |
|---|---|
| Description: | Implements the MST-kNN clustering algorithm which was proposed by Inostroza-Ponta, M. (2008) <https://trove.nla.gov.au/work/28729389?selectedversion=NBD44634158>. |
| Authors: | Jorge Parraga-Alava [aut, cre], Pablo Moscato [aut], Mario Inostroza-Ponta [aut] |
| Maintainer: | Jorge Parraga-Alava <[email protected]> |
| License: | GPL-2 |
| Version: | 0.3.2 |
| Built: | 2024-11-23 06:29:29 UTC |
| Source: | CRAN |
It contains the distances between 84 Indo-European languages based on the mean percent difference in cognacy, using the 200 Swadesh words.
data(dslanguages)data(dslanguages)
An data frame with 84 rows and 84 columns containing a distance matrix.
Once the data set is loaded, it can be accessed as an object of class dataframe called dslanguages.
Dyen, I., Kruskal, J., and Black, P. (1992). An indoeuropean classification: A lexicostatistical experiment. Transactions of the American Philosophical Society. 82, (5).
It contains the expression levels of 2467 genes on 79 samples corresponding to 8 different experiments of the budding yeast: alpha factor (18 samples), cdc15 (15 samples), cold shock (4 samples), diauxic shift (7 samples), DTT shock (4 samples), elutriation (14 samples), heat shock (6 samples) and sporulation (11 samples).
data(dsyeastexpression)data(dsyeastexpression)
An data frame with 2467 rows and 79 columns.
Once the data set is loaded, it can be accessed as an object of class dataframe called dsyeastexpression.
https://www.pnas.org/doi/10.1073/pnas.95.25.14863
M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein. (1998). Cluster analysis and display of genome-wideexpression patterns.Proceedings of the National Academy of Sciences, 95(25):14863–14868
This function generates the k-Nearest Neighbors (kNN) graph which is a subgraph contains edges between nodes if, and only if, they are one of the k nearest neighbors considering the edges costs (distances). Each node represents an object of the complete graph.
generate.knn(edges.complete.graph, suggested.k)generate.knn(edges.complete.graph, suggested.k)
edges.complete.graph |
A object of class "data.frame" with three columns (object_i, object_j, d_ij) representing the distance d_ij between object_i and object_j. |
suggested.k |
It is an optional argument. A numeric value representing the suggested number of k-nearest neighbors to consider to generate the kNN graph. |
During its generation, the k value is automatically determined by the definition:
If suggested.k parameter is not provided, it is not considered by the definition.
A list with the elements
edges.knn.graph |
A object of class "data.frame" with three columns (object_i, object_j, d_ij) representing the d_ij between object_i and object_j that are part of the kNN graph. |
knn.graph |
A object of class "igraph" which is the k-Nearest Neighbors (kNN) graph generated. |
k |
The k value determined by the definition. |
Mario Inostroza-Ponta, Jorge Parraga-Alava, Pablo Moscato
set.seed(1987) ##Generates a data matrix of dimension 50X13 n=50; m=13 x <- matrix(runif(n*m, min = -5, max = 10), nrow=n, ncol=m) ##Computes a distance matrix of x. library("stats") d <- base::as.matrix(stats::dist(x, method="euclidean")) ##Generates complete graph (CG) without suggested.k parameter cg <- generate.complete.graph(1:nrow(x),d) ##Generates kNN graph knn <- generate.knn(cg) ##Visualizing kNN graph plot(knn$knn.graph, main=paste("kNN \n k=", knn$k, sep="")) ##Generates complete graph (CG) with suggested.k parameter cg <- generate.complete.graph(1:nrow(x),d) ##Generates kNN graph knn <- generate.knn(cg, suggested.k=4) ##Visualizing kNN graph plot(knn$knn.graph, main=paste("kNN \n k=", knn$k, sep=""))set.seed(1987) ##Generates a data matrix of dimension 50X13 n=50; m=13 x <- matrix(runif(n*m, min = -5, max = 10), nrow=n, ncol=m) ##Computes a distance matrix of x. library("stats") d <- base::as.matrix(stats::dist(x, method="euclidean")) ##Generates complete graph (CG) without suggested.k parameter cg <- generate.complete.graph(1:nrow(x),d) ##Generates kNN graph knn <- generate.knn(cg) ##Visualizing kNN graph plot(knn$knn.graph, main=paste("kNN \n k=", knn$k, sep="")) ##Generates complete graph (CG) with suggested.k parameter cg <- generate.complete.graph(1:nrow(x),d) ##Generates kNN graph knn <- generate.knn(cg, suggested.k=4) ##Visualizing kNN graph plot(knn$knn.graph, main=paste("kNN \n k=", knn$k, sep=""))
This function generates the Minimal Spanning Tree (MST) graph which is a connected and acyclic subgraph contains all the nodes of the complete graph (CG) and whose edges sum (distances) has minimum costs. Each node represents an object of the complete graph.
generate.mst(edges.complete.graph)generate.mst(edges.complete.graph)
edges.complete.graph |
A object of class "data.frame" with three columns (object_i, object_j, d_ij) representing the distance d_ij between object i and object j of the complete graph. |
Generation of MST graph is performed using the Prim's algorithm.
A list with the elements
edges.mst.graph |
A object of class "data.frame" with three columns (object_i, object_j, d_ij) representing the distance d_ij between object i and object j that are part of the MST graph. |
mst.graph |
A object of class "igraph" which is the Minimal Spanning Tree (MST) graph generated. |
Mario Inostroza-Ponta, Jorge Parraga-Alava, Pablo Moscato
Prim, R.C. (1957). Shortest connection networks and some generalizations. Bell System Technical Journal, 37 1389-1401.
Ignatenkov, E. (2015). Minimum Spanning Tree (MST) for some graph using Prim's MST algorithm. Stanford University course on Coursera.
set.seed(1987) ##Generates a data matrix of dimension 50X13 n=50; m=13 x <- matrix(runif(n*m, min = -5, max = 10), nrow=n, ncol=m) ##Computes a distance matrix of x. library("stats") d <- base::as.matrix(stats::dist(x, method="euclidean")) ##Generates complete graph (CG) cg <- generate.complete.graph(1:nrow(x),d) ##Generates MST graph mstree <- generate.mst(cg) ##Visualizing MST graph plot(mstree$mst.graph, main="MST")set.seed(1987) ##Generates a data matrix of dimension 50X13 n=50; m=13 x <- matrix(runif(n*m, min = -5, max = 10), nrow=n, ncol=m) ##Computes a distance matrix of x. library("stats") d <- base::as.matrix(stats::dist(x, method="euclidean")) ##Generates complete graph (CG) cg <- generate.complete.graph(1:nrow(x),d) ##Generates MST graph mstree <- generate.mst(cg) ##Visualizing MST graph plot(mstree$mst.graph, main="MST")
Performs the MST-kNN clustering algorithm which generates a clustering solution with automatic number of clusters determination using two proximity graphs: Minimal Spanning Tree (MST) and k-Nearest Neighbor (kNN) which are recursively intersected.
To create MST, Prim algorithm is used. To create kNN, distance.matrix passed as input is considered.
mst.knn(distance.matrix, suggested.k)mst.knn(distance.matrix, suggested.k)
distance.matrix |
A numeric matrix or data.frame with equals numbers of rows and columns representing distances between objects to group. |
suggested.k |
It is an optional argument. A numeric value representing the suggested number of k-nearest neighbors to consider during the generating the kNN graph. Note that, due to the algorithm operation, this number may be different during the algorithm execution. |
To see more details of how MST-kNN works refers to the quick guide.
A list with the elements
cnumber |
A numeric value representing the number of clusters of the solution. |
cluster |
A named vector of integers from |
partition |
A partition matrix order by cluster where are shown the objects and the cluster where they are assigned. |
csize |
A vector with the cardinality of each cluster in the solution. |
network |
An object of class "igraph" as a network representing the clustering solution. |
Mario Inostroza-Ponta, Jorge Parraga-Alava, Pablo Moscato
Inostroza-Ponta, M. (2008). An Integrated and Scalable Approach Based on Combinatorial Optimization Techniques for the Analysis of Microarray Data. Ph.D. thesis, School of Electrical Engineering and Computer Science. University of Newcastle.
set.seed(1987) ##load package library("mstknnclust") ##Generates a data matrix of dimension 100X15 n=100; m=15 x <- matrix(runif(n*m, min = -5, max = 10), nrow=n, ncol=m) ##Computes a distance matrix of x. library("stats") d <- base::as.matrix(stats::dist(x, method="euclidean")) ##Performs MST-kNN clustering using euclidean distance. results <- mst.knn(d) ## Visualizes the clustering solution library("igraph") plot(results$network, vertex.size=8, vertex.color=igraph::clusters(results$network)$membership, layout=igraph::layout.fruchterman.reingold(results$network, niter=10000), main=paste("MST-kNN \n Clustering solution \n Number of clusters=",results$cnumber,sep="" ))set.seed(1987) ##load package library("mstknnclust") ##Generates a data matrix of dimension 100X15 n=100; m=15 x <- matrix(runif(n*m, min = -5, max = 10), nrow=n, ncol=m) ##Computes a distance matrix of x. library("stats") d <- base::as.matrix(stats::dist(x, method="euclidean")) ##Performs MST-kNN clustering using euclidean distance. results <- mst.knn(d) ## Visualizes the clustering solution library("igraph") plot(results$network, vertex.size=8, vertex.color=igraph::clusters(results$network)$membership, layout=igraph::layout.fruchterman.reingold(results$network, niter=10000), main=paste("MST-kNN \n Clustering solution \n Number of clusters=",results$cnumber,sep="" ))