Unit 6 - Recitation
setwd('C:/Users/daria.alekseeva/Documents/Edx/Clustering/')
# Video 2
flower = read.csv("flower.csv", header=FALSE)
str(flower)
## 'data.frame': 50 obs. of 50 variables:
## $ V1 : num 0.0991 0.0991 0.1034 0.1034 0.1034 ...
## $ V2 : num 0.112 0.108 0.112 0.116 0.108 ...
## $ V3 : num 0.134 0.116 0.121 0.116 0.112 ...
## $ V4 : num 0.138 0.138 0.121 0.121 0.112 ...
## $ V5 : num 0.138 0.134 0.125 0.116 0.112 ...
## $ V6 : num 0.138 0.129 0.121 0.108 0.112 ...
## $ V7 : num 0.129 0.116 0.103 0.108 0.112 ...
## $ V8 : num 0.116 0.103 0.103 0.103 0.116 ...
## $ V9 : num 0.1121 0.0991 0.1078 0.1121 0.1164 ...
## $ V10: num 0.121 0.108 0.112 0.116 0.125 ...
## $ V11: num 0.134 0.125 0.129 0.134 0.129 ...
## $ V12: num 0.147 0.134 0.138 0.129 0.138 ...
## $ V13: num 0.000862 0.146552 0.142241 0.142241 0.133621 ...
## $ V14: num 0.000862 0.000862 0.142241 0.133621 0.12931 ...
## $ V15: num 0.142 0.142 0.134 0.121 0.116 ...
## $ V16: num 0.125 0.125 0.116 0.108 0.108 ...
## $ V17: num 0.1121 0.1164 0.1078 0.0991 0.0991 ...
## $ V18: num 0.108 0.112 0.108 0.108 0.108 ...
## $ V19: num 0.121 0.129 0.125 0.116 0.116 ...
## $ V20: num 0.138 0.129 0.125 0.116 0.116 ...
## $ V21: num 0.138 0.134 0.121 0.125 0.125 ...
## $ V22: num 0.134 0.129 0.125 0.121 0.103 ...
## $ V23: num 0.125 0.1207 0.1164 0.1164 0.0819 ...
## $ V24: num 0.1034 0.1034 0.0991 0.0991 0.1034 ...
## $ V25: num 0.0948 0.0905 0.0905 0.1034 0.125 ...
## $ V26: num 0.0862 0.0862 0.0991 0.125 0.1422 ...
## $ V27: num 0.086207 0.086207 0.103448 0.12931 0.000862 ...
## $ V28: num 0.0991 0.1078 0.1164 0.1293 0.1466 ...
## $ V29: num 0.116 0.134 0.134 0.121 0.142 ...
## $ V30: num 0.121 0.138 0.142 0.129 0.138 ...
## $ V31: num 0.121 0.134 0.142 0.134 0.129 ...
## $ V32: num 0.116 0.134 0.129 0.116 0.112 ...
## $ V33: num 0.108 0.112 0.116 0.108 0.108 ...
## $ V34: num 0.1078 0.1078 0.1034 0.0991 0.1034 ...
## $ V35: num 0.1078 0.1034 0.0991 0.0991 0.0991 ...
## $ V36: num 0.1078 0.1034 0.1034 0.0905 0.0862 ...
## $ V37: num 0.1078 0.1078 0.1034 0.0819 0.0733 ...
## $ V38: num 0.0948 0.0991 0.0776 0.069 0.0733 ...
## $ V39: num 0.0733 0.056 0.0474 0.0474 0.056 ...
## $ V40: num 0.0474 0.0388 0.0431 0.0474 0.0603 ...
## $ V41: num 0.0345 0.0345 0.0388 0.0474 0.0647 ...
## $ V42: num 0.0259 0.0259 0.0345 0.0431 0.056 ...
## $ V43: num 0.0259 0.0259 0.0388 0.0517 0.0603 ...
## $ V44: num 0.0302 0.0302 0.0345 0.0517 0.0603 ...
## $ V45: num 0.0259 0.0259 0.0259 0.0388 0.0474 ...
## $ V46: num 0.0259 0.0172 0.0172 0.0259 0.0345 ...
## $ V47: num 0.01724 0.01724 0.00862 0.02155 0.02586 ...
## $ V48: num 0.0216 0.0129 0.0129 0.0172 0.0302 ...
## $ V49: num 0.0216 0.0216 0.0216 0.0345 0.0603 ...
## $ V50: num 0.0302 0.0345 0.0388 0.0603 0.0776 ...
# Change the data type to matrix
flowerMatrix = as.matrix(flower)
str(flowerMatrix)
## num [1:50, 1:50] 0.0991 0.0991 0.1034 0.1034 0.1034 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:50] "V1" "V2" "V3" "V4" ...
# Turn matrix into a vector
flowerVector = as.vector(flowerMatrix)
str(flowerVector)
## num [1:2500] 0.0991 0.0991 0.1034 0.1034 0.1034 ...
flowerVector2 = as.vector(flower)
str(flowerVector2)
## 'data.frame': 50 obs. of 50 variables:
## $ V1 : num 0.0991 0.0991 0.1034 0.1034 0.1034 ...
## $ V2 : num 0.112 0.108 0.112 0.116 0.108 ...
## $ V3 : num 0.134 0.116 0.121 0.116 0.112 ...
## $ V4 : num 0.138 0.138 0.121 0.121 0.112 ...
## $ V5 : num 0.138 0.134 0.125 0.116 0.112 ...
## $ V6 : num 0.138 0.129 0.121 0.108 0.112 ...
## $ V7 : num 0.129 0.116 0.103 0.108 0.112 ...
## $ V8 : num 0.116 0.103 0.103 0.103 0.116 ...
## $ V9 : num 0.1121 0.0991 0.1078 0.1121 0.1164 ...
## $ V10: num 0.121 0.108 0.112 0.116 0.125 ...
## $ V11: num 0.134 0.125 0.129 0.134 0.129 ...
## $ V12: num 0.147 0.134 0.138 0.129 0.138 ...
## $ V13: num 0.000862 0.146552 0.142241 0.142241 0.133621 ...
## $ V14: num 0.000862 0.000862 0.142241 0.133621 0.12931 ...
## $ V15: num 0.142 0.142 0.134 0.121 0.116 ...
## $ V16: num 0.125 0.125 0.116 0.108 0.108 ...
## $ V17: num 0.1121 0.1164 0.1078 0.0991 0.0991 ...
## $ V18: num 0.108 0.112 0.108 0.108 0.108 ...
## $ V19: num 0.121 0.129 0.125 0.116 0.116 ...
## $ V20: num 0.138 0.129 0.125 0.116 0.116 ...
## $ V21: num 0.138 0.134 0.121 0.125 0.125 ...
## $ V22: num 0.134 0.129 0.125 0.121 0.103 ...
## $ V23: num 0.125 0.1207 0.1164 0.1164 0.0819 ...
## $ V24: num 0.1034 0.1034 0.0991 0.0991 0.1034 ...
## $ V25: num 0.0948 0.0905 0.0905 0.1034 0.125 ...
## $ V26: num 0.0862 0.0862 0.0991 0.125 0.1422 ...
## $ V27: num 0.086207 0.086207 0.103448 0.12931 0.000862 ...
## $ V28: num 0.0991 0.1078 0.1164 0.1293 0.1466 ...
## $ V29: num 0.116 0.134 0.134 0.121 0.142 ...
## $ V30: num 0.121 0.138 0.142 0.129 0.138 ...
## $ V31: num 0.121 0.134 0.142 0.134 0.129 ...
## $ V32: num 0.116 0.134 0.129 0.116 0.112 ...
## $ V33: num 0.108 0.112 0.116 0.108 0.108 ...
## $ V34: num 0.1078 0.1078 0.1034 0.0991 0.1034 ...
## $ V35: num 0.1078 0.1034 0.0991 0.0991 0.0991 ...
## $ V36: num 0.1078 0.1034 0.1034 0.0905 0.0862 ...
## $ V37: num 0.1078 0.1078 0.1034 0.0819 0.0733 ...
## $ V38: num 0.0948 0.0991 0.0776 0.069 0.0733 ...
## $ V39: num 0.0733 0.056 0.0474 0.0474 0.056 ...
## $ V40: num 0.0474 0.0388 0.0431 0.0474 0.0603 ...
## $ V41: num 0.0345 0.0345 0.0388 0.0474 0.0647 ...
## $ V42: num 0.0259 0.0259 0.0345 0.0431 0.056 ...
## $ V43: num 0.0259 0.0259 0.0388 0.0517 0.0603 ...
## $ V44: num 0.0302 0.0302 0.0345 0.0517 0.0603 ...
## $ V45: num 0.0259 0.0259 0.0259 0.0388 0.0474 ...
## $ V46: num 0.0259 0.0172 0.0172 0.0259 0.0345 ...
## $ V47: num 0.01724 0.01724 0.00862 0.02155 0.02586 ...
## $ V48: num 0.0216 0.0129 0.0129 0.0172 0.0302 ...
## $ V49: num 0.0216 0.0216 0.0216 0.0345 0.0603 ...
## $ V50: num 0.0302 0.0345 0.0388 0.0603 0.0776 ...
# Compute distances
distance = dist(flowerVector, method = "euclidean")
# Video 3
# Hierarchical clustering
clusterIntensity = hclust(distance, method="ward")
## The "ward" method has been renamed to "ward.D"; note new "ward.D2"
# Plot the dendrogram
plot(clusterIntensity)
# Select 3 clusters
rect.hclust(clusterIntensity, k = 3, border = "red")
flowerClusters = cutree(clusterIntensity, k = 3)
flowerClusters
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [35] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [69] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [103] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [137] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [171] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [205] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [239] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [273] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [307] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [341] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3
## [375] 2 2 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [409] 1 1 1 1 1 1 1 1 1 2 2 1 1 1 3 3 3 3 3 2 1 2 3 2 1 1 1 1 1 1 1 1 1 1
## [443] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 1 1 1 3 3 3 3
## [477] 3 2 2 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [511] 1 1 1 1 1 1 2 3 3 2 1 1 3 3 3 3 3 2 3 3 3 1 2 3 3 1 1 1 1 1 1 1 1 1
## [545] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 2 1 2 3 3 3 1 1 3 3 3 3 3 2
## [579] 3 3 2 2 3 3 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [613] 2 3 3 2 1 3 3 3 2 1 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1
## [647] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 2 2 3 3 3 1 2 3 3 3 3 3 3 3
## [681] 3 3 3 3 3 3 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
## [715] 3 3 3 3 3 3 3 2 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 1 1 1 1 1 1 1 1 1 1
## [749] 1 1 1 1 1 1 1 1 1 1 1 2 3 2 1 1 2 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3
## [783] 3 3 3 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 2 1 1
## [817] 3 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 2 1 1 2 2 3 3 1 1 1 1 1 1 1 1
## [851] 1 1 1 1 1 1 1 1 1 1 2 3 3 3 2 1 2 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3
## [885] 2 1 2 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 2 2
## [919] 3 3 3 3 3 2 2 3 3 3 3 3 3 3 3 2 1 3 3 3 3 3 3 2 1 1 1 1 1 1 1 1 1 1
## [953] 1 1 1 1 1 1 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 3 3 3 2 2 3 3
## [987] 3 3 3 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3
## [1021] 3 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1055] 1 1 2 2 2 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 2 2
## [1089] 2 2 3 3 3 2 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2
## [1123] 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 1 1 1 1 1 1 1 1 1 1 1 1
## [1157] 2 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 2
## [1191] 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 3 3 3 3 3 2 2 2 2 2 2
## [1225] 2 2 2 2 2 2 2 3 3 3 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1259] 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1
## [1293] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2
## [1327] 2 2 2 2 3 3 3 3 3 3 3 3 3 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3
## [1361] 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 1
## [1395] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2
## [1429] 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3
## [1463] 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 2 1 1 1 1 1
## [1497] 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 2 2 3 3 3 2 3
## [1531] 3 3 3 3 3 2 3 3 3 3 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 2 2 1
## [1565] 2 3 3 3 3 3 3 3 3 2 2 3 3 3 3 2 3 3 3 3 3 2 1 2 2 2 2 2 1 1 1 1 1 1
## [1599] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 2 3 3
## [1633] 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3
## [1667] 2 3 3 3 3 3 2 2 3 3 3 3 3 2 1 3 3 3 3 3 3 3 2 1 1 1 1 1 1 1 1 1 1 1
## [1701] 1 1 1 1 1 1 1 1 1 1 1 2 3 3 2 2 3 3 3 3 3 3 1 3 3 3 3 3 3 2 1 2 3 3
## [1735] 3 3 3 3 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 2 1 3 3 3
## [1769] 3 3 3 1 1 3 3 3 3 3 3 2 1 1 2 3 3 3 3 2 3 2 1 1 1 1 1 1 1 1 1 1 1 1
## [1803] 1 1 1 1 1 1 1 1 1 2 2 1 2 3 3 3 3 3 2 1 2 3 3 2 3 3 3 2 1 1 1 1 3 3
## [1837] 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3
## [1871] 1 1 2 3 3 2 3 3 3 3 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1905] 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 3 1 1 2 3 3 1 3 3 3 3 1 1 1 1 1 1 1 1
## [1939] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 3 2 1 1
## [1973] 2 3 3 1 2 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2007] 1 1 1 1 1 1 1 1 1 1 1 2 3 1 1 1 1 3 3 1 1 3 3 3 1 1 1 1 1 1 1 1 1 1
## [2041] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
## [2075] 3 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2109] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2143] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2177] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2211] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2245] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2279] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2313] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2347] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2381] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2415] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2449] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [2483] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
# Find mean intensity values
tapply(flowerVector, flowerClusters, mean)
## 1 2 3
## 0.08574315 0.50826255 0.93147713
# Plot the image and the clusters
dim(flowerClusters) = c(50,50)
image(flowerClusters, axes = FALSE)
# Original image
image(flowerMatrix,axes=FALSE,col=grey(seq(0,1,length=256)))
# Video 4
# Let's try this with an MRI image of the brain
healthy = read.csv("healthy.csv", header=FALSE)
healthyMatrix = as.matrix(healthy)
str(healthyMatrix)
## num [1:566, 1:646] 0.00427 0.00855 0.01282 0.01282 0.01282 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:646] "V1" "V2" "V3" "V4" ...
# Plot image
image(healthyMatrix,axes=FALSE,col=grey(seq(0,1,length=256)))
# Hierarchial clustering
healthyVector = as.vector(healthyMatrix)
#distance = dist(healthyVector, method = "euclidean")
# We have an error - why?
str(healthyVector)
## num [1:365636] 0.00427 0.00855 0.01282 0.01282 0.01282 ...
# Video 5
# Specify number of clusters
k = 5
# Run k-means
set.seed(1)
KMC = kmeans(healthyVector, centers = k, iter.max = 1000)
str(KMC)
## List of 9
## $ cluster : int [1:365636] 3 3 3 3 3 3 3 3 3 3 ...
## $ centers : num [1:5, 1] 0.4818 0.1062 0.0196 0.3094 0.1842
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:5] "1" "2" "3" "4" ...
## .. ..$ : NULL
## $ totss : num 5775
## $ withinss : num [1:5] 96.6 47.2 39.2 57.5 62.3
## $ tot.withinss: num 303
## $ betweenss : num 5472
## $ size : int [1:5] 20556 101085 133162 31555 79278
## $ iter : int 2
## $ ifault : int 0
## - attr(*, "class")= chr "kmeans"
# Extract clusters
healthyClusters = KMC$cluster
KMC$centers[2]
## [1] 0.1061945
# Plot the image with the clusters
dim(healthyClusters) = c(nrow(healthyMatrix), ncol(healthyMatrix))
image(healthyClusters, axes = FALSE, col=rainbow(k))
# Video 6
# Apply to a test image
tumor = read.csv("tumor.csv", header=FALSE)
tumorMatrix = as.matrix(tumor)
tumorVector = as.vector(tumorMatrix)
# Apply clusters from before to new image, using the flexclust package
#install.packages("flexclust")
library(flexclust)
## Loading required package: grid
## Loading required package: lattice
## Loading required package: modeltools
## Loading required package: stats4
KMC.kcca = as.kcca(KMC, healthyVector)
tumorClusters = predict(KMC.kcca, newdata = tumorVector)
# Visualize the clusters
dim(tumorClusters) = c(nrow(tumorMatrix), ncol(tumorMatrix))
image(tumorClusters, axes = FALSE, col=rainbow(k))
