[PDF] accuracy of k means clustering in r

As K-means is an unsupervised learning algorithm, all data points are included. The scatterplot for predicted class shows a clear boundary to separate the data points into two pre-defined cluster. A confusion matrix of the results of k-means algorithm is created and an accuracy of 66.45% is computed.
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  • How accurate is k-means clustering?

    The experiment shows that by using clustering technique in pre-processing stage for re-tagging response classes, the Decision tree is able to achieve 97.5% recognition accuracy in classification, better than the 81.95% recognition accuracy when using Decision tree alone.

  • How to evaluate k-means clustering in R?

    You can evaluate the clusters by looking at $totss and $betweenss. R comes with a default K Means function, kmeans(). It only requires two inputs: a matrix or data frame of all numeric values and a number of centers (i.e. your number of clusters or the K of k means). X is your data frame or matrix.

  • How can you improve the accuracy of k-means clustering?

    K-means clustering algorithm can be significantly improved by using a better initialization technique, and by repeating (re-starting) the algorithm. When the data has overlapping clusters, k-means can improve the results of the initialization technique.
  • The optimal number of clusters k is the one that maximize the average silhouette over a range of possible values for k (Kaufman and Rousseeuw 1990). The algorithm is similar to the elbow method and can be computed as follow: Compute clustering algorithm (e.g., k-means clustering) for different values of k.
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