Clustering is an unsupervised method for grouping patterns based on similarity such that the…
Clustering is an unsupervised method for grouping patterns based on similarity such that the patterns in the same group are like one another, and those in different groups are less like one another. Clustering has many uses, including outlier detection and unsupervised image segmentation. The K-Means clustering algorithm is one of the most widely used clustering algorithms. In this assignment, you are going to implement basic k-means and analyze the effect of normalization. Implement basic K-means algorithm to cluster the sample data set. Do not use built-in function such as kmeans for clustering. Two sample data sets (kmtest and iris) are provided. Make sure your program works with both data sets. A. What to DO: 1. Clustering with K-means algorithm for kmtest dataset. a. Without normalization, cluster the dataset by choosing the K value as 2, 3, 4, 5. Plot results for each K values by showing each cluster with different color and cluster centers. b. With normalization, cluster the dataset by choosing the K values as 2, 3, 4, 5. You should create clustering centers and clustering input for normalized data. Use z-score normalization as the normalization method. First normalize the data and apply clustering on the normalized data. Plot results for each K values by showing each cluster with different color and cluster centers.
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