What Farthest neighbor clustering is
Farthest neighbor clustering is a type of unsupervised learning algorithm used for clustering data points. It is also known as the single-linkage clustering algorithm. This algorithm works by grouping data points together by iteratively finding the most distant data points and combining them into the same cluster.
Steps for Farthest Neighbor Clustering
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Start by selecting a point at random from the data set to be the first cluster.
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Calculate the distance between the chosen point and all other points in the data set.
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Select the point with the greatest distance from the chosen point and add it to the cluster.
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Calculate the distance between the new point and all other points in the data set.
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Select the point with the greatest distance from the new point and add it to the cluster.
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Repeat steps 4 and 5 until the desired number of clusters is reached.
Examples
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Farthest Neighbor Clustering can be used to group customers according to their spending habits. By analyzing purchase history, it can be used to identify and group customers who have similar spending habits.
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Farthest Neighbor Clustering can be used to identify groups of similar items. For example, it can be used to identify groups of similar products in a store, or to identify groups of similar items in an online marketplace.
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Farthest Neighbor Clustering can be used to identify groups of similar genes. By analyzing gene expression data, it can be used to identify and group genes that have similar expression patterns.