Clustering: essence and objectives
Posted: Tue Dec 17, 2024 6:23 am
GeekBrains website editors
Chief Editor of the Programming Section
What are we talking about? Clustering is the grouping of data by certain criteria: type, size, shape, category, etc. At the same time, objects within a cluster should minimally converge on one feature, differing on the others.
Where is it used? Clustering is used in various fields: marketing, programming, medicine, SEO. There are two popular methods: K-Means and DBSCAN. It is also performed in Excel or some programming languages.
The essence of clustering
Clustering is a unique method that allows you targeted industry database to divide objects into groups without having a pre-set training sample or knowledge of the nature of these classes. The model independently determines the similarity of some objects and combines them into one sector. One of the advantages of clustering is that it does not require knowledge of which classes will be formed and how many there will be. The scientific name for clustering is unsupervised classification - due to the similarity of the problem statement.
The essence of clustering
The essence of clustering
Clustering methods are an effective tool for solving classification problems when collecting a training sample is difficult or expensive. The validation sample set requires fewer examples to evaluate the results of the process. But it should be remembered that the accuracy of supervised methods is significantly higher. And if collecting a training sample is possible, it is better to use it to solve the classification problem.
One good example of the application of clustering methods is the analysis of geodata. When using applications on mobile phones, it is often necessary to determine the exact location. The error in GPS data arises from the movement of users: often it is necessary to observe many points instead of the exact position. This is especially relevant when analyzing the behavior of thousands of people in a certain location, for example, to determine the most popular places where users get into a taxi at the airport.
Yandex.Taxi used clustering of order point coordinates to determine convenient places to call a taxi. Class centers were used as pickup point candidates, highlighted in the application. Simple filters were also taken into account to exclude points in buildings and water. Manually set pickup points were used, for example, near airports.
Another example related to geodata clustering is the presence of interfaces for viewing photos in phones. They can be used to determine the location, and when zooming the map, you can see the different clusters into which they are divided. Another interesting example is the construction of a color scheme for the interface for the image selected by the user: it is necessary to cluster the colors using the RGB representation or other shade features. Then use them to design the interface, including the background image.
Clustering tasks
This approach is used when there are data arrays with different features. However, they must have some unity – otherwise it will not be possible to implement clustering. The following can be divided into groups:
Clients, in order to analyze the behavior of their specific associations.
Business opponents - when studying the market.
Diseases - to study statistical data on recovery.
Survey participants – to analyze the opinions of people from different groups.
SEO keys – for creating topics on website pages.
The resulting files have different formats for their convenient processing.
Chief Editor of the Programming Section
What are we talking about? Clustering is the grouping of data by certain criteria: type, size, shape, category, etc. At the same time, objects within a cluster should minimally converge on one feature, differing on the others.
Where is it used? Clustering is used in various fields: marketing, programming, medicine, SEO. There are two popular methods: K-Means and DBSCAN. It is also performed in Excel or some programming languages.
The essence of clustering
Clustering is a unique method that allows you targeted industry database to divide objects into groups without having a pre-set training sample or knowledge of the nature of these classes. The model independently determines the similarity of some objects and combines them into one sector. One of the advantages of clustering is that it does not require knowledge of which classes will be formed and how many there will be. The scientific name for clustering is unsupervised classification - due to the similarity of the problem statement.
The essence of clustering
The essence of clustering
Clustering methods are an effective tool for solving classification problems when collecting a training sample is difficult or expensive. The validation sample set requires fewer examples to evaluate the results of the process. But it should be remembered that the accuracy of supervised methods is significantly higher. And if collecting a training sample is possible, it is better to use it to solve the classification problem.
One good example of the application of clustering methods is the analysis of geodata. When using applications on mobile phones, it is often necessary to determine the exact location. The error in GPS data arises from the movement of users: often it is necessary to observe many points instead of the exact position. This is especially relevant when analyzing the behavior of thousands of people in a certain location, for example, to determine the most popular places where users get into a taxi at the airport.
Yandex.Taxi used clustering of order point coordinates to determine convenient places to call a taxi. Class centers were used as pickup point candidates, highlighted in the application. Simple filters were also taken into account to exclude points in buildings and water. Manually set pickup points were used, for example, near airports.
Another example related to geodata clustering is the presence of interfaces for viewing photos in phones. They can be used to determine the location, and when zooming the map, you can see the different clusters into which they are divided. Another interesting example is the construction of a color scheme for the interface for the image selected by the user: it is necessary to cluster the colors using the RGB representation or other shade features. Then use them to design the interface, including the background image.
Clustering tasks
This approach is used when there are data arrays with different features. However, they must have some unity – otherwise it will not be possible to implement clustering. The following can be divided into groups:
Clients, in order to analyze the behavior of their specific associations.
Business opponents - when studying the market.
Diseases - to study statistical data on recovery.
Survey participants – to analyze the opinions of people from different groups.
SEO keys – for creating topics on website pages.
The resulting files have different formats for their convenient processing.