User Knowledge Modeling – Using K-means Clustering

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User Knowledge Modeling – Using K-means Clustering

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dataProducts_pitch

On Github ys-huang / dataProducts_pitch

User Knowledge Modeling

Using K-means Clustering

https://eg7649.shinyapps.io/developingDataProducts

SEAN HUANG

What does it do?

  • This simple app aims to provide a visual tool for showing result of k-means altorithm.
  • You can assign two attributes from datasets as well as cluster number you prefer.
  • Server will do the calculation based on the attributes and cluster number you choose, and show you the result on a 2-dimention plot.

User Knowledge Modeling Dataset?

  • It is the real dataset about the students' knowledge status about the subject of Electrical DC Machines. The dataset had been obtained from Ph.D. Thesis.

  • Attribute information

    • STG: The degree of study time for goal object materails
    • SCG: The degree of repetition number of user for goal object materails
    • STR: The degree of study time of user for related objects with goal object
    • LPR: The exam performance of user for related objects with goal object
    • PEG: The exam performance of user for goal objects
    • UNS: The knowledge level of user (output)

  • You can download the dataset here.

How does it work?

User select two attributes and cluster number UI pass it to server as parameters for kmeans calculation Draw a coloful plot accroding to the result Return the plot to the page so user can see what it will be according to his choice 
shinyServer(function(input, output, session) {
  selectedData <- reactive({
    data[, c(input$xcol, input$ycol)] }) # select data accroding to user input
  clusters <- reactive({
    kmeans(selectedData(), input$clusters) }) # do the calculation
  output$plot1 <- renderPlot({ # print colorful result
    par(mar = c(5.1, 4.1, 0, 1))
    plot(selectedData(), col = clusters()$cluster, pch = 20, cex = 3)
    points(clusters()$centers, pch = 13, cex = 3, lwd = 4, col = "#CC00CD") })
})

What will you see?

Choose STG vs. STR 4 cluster

selectedData <- data[, c(1,3)]
clusters <- kmeans(selectedData, 4)
par(mar = c(5.1, 4.1, 0, 1))
plot(selectedData, col = clusters$cluster, pch = 20, cex = 3)
points(clusters$centers, pch = 13, cex = 3, lwd = 4, col = "#CC00CD")

Choose SCG vs. STR 2 cluster

selectedData <- data[, 2:3]
clusters <- kmeans(selectedData, 2)
par(mar = c(5.1, 4.1, 0, 1))
plot(selectedData, col = clusters$cluster, pch = 20, cex = 3)
points(clusters$centers, pch = 13, cex = 3, lwd = 4, col = "#CC00CD")