Comparing Clustering Algorithms and Distance Metrics in Machine Learning
Explore the k-means clustering algorithm and other prominent techniques in machine learning. Dive into the similarities, differences, advantages, and disadvantages of algorithms like k-means++, canopy clustering, and farthest-first clustering. Learn about essential distance metrics such as Euclidean and Manhattan distances impacting clustering outcomes.
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Presentation Transcript
The k-mean clustering algorithm is one of many clustering algorithms used in machine learning. Each algorithm has advantages, disadvantages, and specific applications. In this section, we'll look at the similarities and differences between the k-mean clustering algorithm and other prominent clustering techniques.
Random clustering Algorithm ? 2 min?????? ?? ?=1 k-means++ clustering Algorithm 2 ??= ????:1 ??? ??
Canopy Clustering Algorithm 1. Stage 1: Use a "unexpansive" distance measurement to approximately divide the data into overlapping subsets known as canopies. This is done using two thresholds, T1 (the loose distance) and T2 (the tight distance), where T1>T2. 2. Stage 2: Perform elaborate clustering inside each canopy by using an "expensive" distance measurement.
Farthest first clustering algorithm : ? ?,? = ???? ?,? ??(?,?) (?,?) represents the distance between the elements ? ? and ? ? . ? and ? represent two groups of items (clusters).
Distance Metric: The distance captures a resemblance. Distance metrics are used by clustering algorithms such as K-means clustering to quantify the similarity between any two data points. Choosing the type of distance metric has a significant impact on the clustering outcomes.
Euclidean distance: ? (??? ???)2 ???= ?=1 Manhattan Distance: ? ?????,? = ?? ?? ?=1
