The K-means method minimises the distance between points inside a group in order to group data points together. The total of the absolute differences between two sites is computed using the L1 distance metric.
Using the L1 distance measure and the K-means method, the points (1, 0, 1), (1, 1, 0), (0, 0, 1), and (1, 1, 1) would be grouped together.
The k-means algorithm is a clustering technique that minimises the distance between points inside a group to group data points together. To begin, k sites are chosen at random to serve as cluster centres by the algorithm. Each data point is then assigned to the cluster centre nearest to it. The technique recalculates the cluster centres by calculating the mean of all the data points in the cluster once each data point has been assigned to a cluster.
Until the cluster centres cease to fluctuate, this process is repeated. Because it is easy to use and works with any kind of data, the k-means algorithm is a widely used clustering technique. Nevertheless, the method can occasionally yield less-than-ideal outcomes since it is sensitive to the selection of k.
The K-means method minimises the distance between points inside a group in order to group data points together. The total of the absolute differences between two sites is computed using the L1 distance metric.
Using the L1 distance measure and the K-means method, the points (1, 0, 1), (1, 1, 0), (0, 0, 1), and (1, 1, 1) would be grouped together.
Question:-
Summarize the K-means algorithm and group the points (1,0, 1),(1,1,0),(0,0,1), and (1,1,1) using the K-means algorithm. Use the L1 distance measure.