inst/doc/LearnClust.R

In LearnClust: Learning Hierarchical Clustering Algorithms

## ----setup, include=FALSE-----------------------------------------------------
library(LearnClust)
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

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cluster1 <- matrix(c(1,2),ncol=2)

cluster2 <- matrix(c(2,4),ncol=2)

weight <- c(0.2,0.8)

vectorData <- c(1,1,2,3,4,7,8,8,8,10)
# vectorData <- c(1:10)

matrixData <- matrix(vectorData,ncol=2,byrow=TRUE)
print(matrixData)

dfData <- data.frame(matrixData)
print(dfData)
plot(dfData)

cMatrix <- matrix(c(2,4,4,2,3,5,1,1,2,2,5,5,1,0,1,1,2,1,2,4,5,1,2,1), ncol=3, byrow=TRUE)

cDataFrame <- data.frame(cMatrix)


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edistance(cluster1,cluster2)

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mdistance(cluster1,cluster2)

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canberradistance(cluster1,cluster2)

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chebyshevDistance(cluster1,cluster2)

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octileDistance(cluster1,cluster2)

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edistanceW(cluster1,cluster2,weight)

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mdistanceW(cluster1,cluster2,weight)

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canberradistanceW(cluster1,cluster2,weight)

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chebyshevDistanceW(cluster1,cluster2,weight)

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octileDistanceW(cluster1,cluster2,weight)

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list <- toList(vectorData)

# list <- toList(matrixData)

# list <- toList(dfData)

print(list)

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matrixDistance <- mdAgglomerative(list,'MAN','AVG')
print(matrixDistance)

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minDistance <- minDistance(matrixDistance)
print(minDistance)

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groupedClusters <- getCluster(minDistance, matrixDistance)
print(groupedClusters)

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updatedClusters <- newCluster(list, groupedClusters)
print(updatedClusters)

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agglomerativeExample <- agglomerativeHC(dfData,'EUC','MAX')

plot(agglomerativeExample$dendrogram)
print(agglomerativeExample$clusters)
print(agglomerativeExample$groupedClusters)

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cleanClusters <- usefulClusters(updatedClusters)
print(cleanClusters)

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distances <- c(2,4,6,8)

clusterDistanceByApproach <- clusterDistanceByApproach(distances,'AVG')
print(clusterDistanceByApproach)

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clusterDistance <- clusterDistance(cluster1,cluster2,'MAX','MAN')
print(clusterDistance)

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list <- toList.details(vectorData)

# list <- toList(matrixData)

# list <- toList(dfData)

print(list)

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matrixDistance <- mdAgglomerative.details(list,'MAN','AVG')

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minDistance <- minDistance.details(matrixDistance)

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groupedClusters <- getCluster.details(minDistance, matrixDistance)

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updatedClusters <- newCluster.details(list, groupedClusters)

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agglomerativeExample <- agglomerativeHC.details(vectorData,'EUC','MAX')

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 # list <- toListDivisive(vectorData)

# list <- toListDivisive(matrixData)

 list <- toListDivisive(dfData[1:4,])

print(list)

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clustersList <- initClusters(list)
print(clustersList)

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matrixDistance <- mdDivisive(clustersList,'MAN','AVG',list)
print(matrixDistance)

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maxDistance <- maxDistance(matrixDistance)
print(maxDistance)

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dividedClusters <- getClusterDivisive(maxDistance, matrixDistance)
print(dividedClusters)

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divisiveExample <- divisiveHC(dfData[1:4,],'MAN','AVG')
print(divisiveExample)

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data <- c(1,2,1,3,1,4,1,5)

components <- toListDivisive(data)

cluster1 <- matrix(c(1,2,1,3),ncol=2,byrow=TRUE)
cluster2 <- matrix(c(1,4,1,5),ncol=2,byrow=TRUE)
cluster3 <- matrix(c(1,6,1,7),ncol=2,byrow=TRUE)

complementaryClusters(components,cluster1,cluster2)

complementaryClusters(components,cluster1,cluster3)


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complementaryClusters.details(components,cluster1,cluster2)

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# list <- toListDivisive.details(vectorData)

# list <- toListDivisive(matrixData)

 list <- toListDivisive(dfData[1:4,])

print(list)

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clustersList <- initClusters.details(list)

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matrixDistance <- mdDivisive.details(clustersList,'MAN','AVG',list)

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maxDistance <- maxDistance.details(matrixDistance)

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dividedClusters <- getClusterDivisive.details(maxDistance, matrixDistance)

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divisiveExample <- divisiveHC.details(dfData[1:4,],'MAN','AVG')
print(divisiveExample)


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initData <- initData(cDataFrame)
print(initData)

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target <- c(1,2,3)

initTarget <- initTarget(target,cDataFrame)
print(initTarget)

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weight <- c(5,7,6)

weights <- normalizeWeight(TRUE,weight,cDataFrame)
print(weights)

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cluster1 <- matrix(c(1,2,3),ncol=3)
cluster2 <- matrix(c(2,5,8),ncol=3)

weight <- c(3,7,4)

distance <- distances(cluster1,cluster2,'CHE',weight)
print(distance)

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target <- c(5,5,1)

weight <- c(3,7,5)

correlation <- correlationHC(cDataFrame, target,  weight)

print(correlation$sortedValues)

print(correlation$distances)

plot(correlation$dendrogram)

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initData <- initData.details(cDataFrame)

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targetValid <- c(1,2,3)

targetInvalid <- c(1,2)

initTarget <- initTarget.details(targetValid,cDataFrame)

initTarget <- initTarget.details(targetInvalid,cDataFrame)

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weight <- c(5,7,6)

weights <- normalizeWeight.details(TRUE,weight,cDataFrame)

weights <- normalizeWeight.details(FALSE,weight,cDataFrame)

weights <- normalizeWeight.details(FALSE,NULL,cDataFrame)

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cluster1 <- matrix(c(1,2,3),ncol=3)
cluster2 <- matrix(c(2,5,8),ncol=3)

weight <- c(3,7,4)

distance <- distances.details(cluster1,cluster2,'CHE',weight)

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target <- c(5,5,1)

weight <- c(3,7,5)

correlation <- correlationHC.details(cDataFrame, target,  weight)

print(correlation$sortedValues)

print(correlation$distances)

plot(correlation$dendrogram)