Popular Clustering Algorithms
Posted: Tue Dec 17, 2024 6:23 am
There are many clustering methods, and different methods produce different results. Let's study the following most common methods for solving real problems:
K-Means
To use the method, you need to adhere to a certain clustering algorithm:
Determine the parameter k – the number of groups we would like to obtain.
Using random sampling, we determine k object points from all available data (centroids).
We will perform the process of determining which potential cluster center is closest to each data point. A cluster is formed by all points closest to the same centroid.
In each such group we find the middle among targeted industry database the coordinates, that is, the middle one – now this is the new potential center of the cluster.
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We continue to calculate the distances between each point-object and new centroids, determine the closest ones, and calculate new potential centers of new clusters. These activities must be carried out until the centroids change.
DBSCAN
For this method, the number of clusters is set automatically. However, it is necessary to define the range of search points and their minimum number in the cluster.
Let's select an object and find points around it in the range we've chosen.
If there is not even a minimal amount of them, we will call them outliers and will not assign them to any group.
If we managed to find the required number of them, we also look for new points in the range we have chosen for each one. Thus, all of them that are located at a distance from each other less than or equal to the specified range will form one cluster.
However, the choice of approach to use the method is not always obvious. Depending on the task and the characteristics of the data, different clustering methods are more or less effective. The DBSCAN method is especially useful when the data contains objects that do not belong to any group and the clusters have a complex shape. Determining the number of classes K-Means is one of the most popular clustering methods and is usually used when their number to be found in the data is known in advance. It works well with linearly separable data, when it is possible to draw lines separating the clusters.
Popular Clustering Algorithms
Popular Clustering Algorithms
If the problem requires determining the exact number of groups, K-Means may be more appropriate. But if the data cannot be separated by linear lines, then DBSCAN has advantages. The image below shows that DBSCAN is more accurate in determining clusters when the data are not separable in this way than K-Means.
K-Means
To use the method, you need to adhere to a certain clustering algorithm:
Determine the parameter k – the number of groups we would like to obtain.
Using random sampling, we determine k object points from all available data (centroids).
We will perform the process of determining which potential cluster center is closest to each data point. A cluster is formed by all points closest to the same centroid.
In each such group we find the middle among targeted industry database the coordinates, that is, the middle one – now this is the new potential center of the cluster.
A gift for you! Freely available until
22.12
Download TOP-10
free neural networks
for marketers
Reduce time spent on creating offers and analytics by 30%
To receive the file, please enter your e-mail:
E-mail, for example, [email protected]
Please confirm that you are not a robot by providing your phone number:
+7
912 345-67-89
Download the collection for free
I confirm my consent to the processing of personal data
We continue to calculate the distances between each point-object and new centroids, determine the closest ones, and calculate new potential centers of new clusters. These activities must be carried out until the centroids change.
DBSCAN
For this method, the number of clusters is set automatically. However, it is necessary to define the range of search points and their minimum number in the cluster.
Let's select an object and find points around it in the range we've chosen.
If there is not even a minimal amount of them, we will call them outliers and will not assign them to any group.
If we managed to find the required number of them, we also look for new points in the range we have chosen for each one. Thus, all of them that are located at a distance from each other less than or equal to the specified range will form one cluster.
However, the choice of approach to use the method is not always obvious. Depending on the task and the characteristics of the data, different clustering methods are more or less effective. The DBSCAN method is especially useful when the data contains objects that do not belong to any group and the clusters have a complex shape. Determining the number of classes K-Means is one of the most popular clustering methods and is usually used when their number to be found in the data is known in advance. It works well with linearly separable data, when it is possible to draw lines separating the clusters.
Popular Clustering Algorithms
Popular Clustering Algorithms
If the problem requires determining the exact number of groups, K-Means may be more appropriate. But if the data cannot be separated by linear lines, then DBSCAN has advantages. The image below shows that DBSCAN is more accurate in determining clusters when the data are not separable in this way than K-Means.