Methods of Clustering
Clustering methods can be classified into the following types:
- Partitioning Method
- Hierarchical Method
- Density-Based Method
- Grid-Based Method
- Model-Based Method
- Constraint-Based Method
Partitioning Method
It's used to divide data into divisions so that clusters can be formed. When "n" partitions are applied to "p" database items, each partition is represented by a cluster, and n < p.
The following are the two requirements for using this Partitioning Clustering Method:
- A single objective should be assigned to a single group.
- There should be no such thing as a group without a clear goal.
There is a technique called iterative relocation in the partitioning process, which means the object will be relocated from one group to another to improve the partitioning.
Hierarchical Method
A hierarchical decomposition of the specified set of data objects is constructed using this method. On the basis of how the hierarchical decomposition is generated, we may classify hierarchical approaches and determine the goal of categorization.
For the production of hierarchical decomposition, there are two types of approaches:
- Bottom-up Technique: The bottom-up approach is also known as the agglomerative approach. The given data is initially separated into groups, where the objects form independent groups. Following that, it keeps merging objects or groups that are close to one another, indicating that they have comparable properties. This process of merging continues until the termination condition is met.
- The Top-Down Technique: The top-down approach is also known as the dividing approach. Starting with data items in the same cluster would be the first step in this method. Continuous iteration divides a large set of individual clusters into small clusters. The loop continues until either the termination condition is met or each cluster has one object.
Density-Based Method
Density is the major emphasis of the density-based technique. The provided cluster will continue to develop indefinitely in this manner as long as the density in the neighborhood reaches a certain threshold, i.e. for each data point within a specific cluster. A particular cluster's radius must contain at least a certain number of points.
Grid-Based Method
A grid is produced utilizing the objects together in the Grid-Based approach, which quantizes the object space into a finite number of cells that form a grid structure. Fast processing time is one of the key advantages of the grid-based technique, which is only limited by the number of cells in each dimension of the quantized space. This approach has a significantly faster processing time, therefore it can save time.
Model-Based Method
All clusters are hypothesized in the model-based procedure in order to discover the data that is most suitable for the model. The density function's clustering is used to locate the clusters for a particular model. It depicts the spatial distribution of data points and also gives a method for calculating the number of clusters using standard statistics while accounting for outliers or noise. As a result, it produces reliable clustering approaches.
Constraint-Based Method
The application or user-oriented constraints are incorporated into the constraint-based clustering method. A constraint refers to the user's expectation or the desired clustering results' qualities. Constraints allow us to communicate with the clustering process in a more participatory way. The user or the application requirement can specify constraints.
Applications of Clustering
Clustering has a huge number of applications in a variety of fields. Clustering is used in a number of ways, including:
- Engines that make recommendations
- Segmentation of the market
- Analyzing social networks
- Grouping of search results
- Imaging in medicine
- Segmentation of images
- Detecting anomalies
FAQs
1. What are the different types of clustering algorithms?
- K-means clustering
- Mini-batch K-means clustering
- BIRCH
- DBSCAN
- Mean shift
- OPTICS
2. What is clustering in Data Mining?
Clustering is an unsupervised Machine Learning-based Algorithm that divides a set of data points into clusters, allowing the objects to be grouped together. Clustering aids in the division of data into subsets.
3. Why clustering is important in real-life applications?
Many firms utilize cluster analysis to find consumers who are similar to one another so that they may modify the emails they send to them to maximize income. For example, a company might gather the following information about its customers: The percentage of emails that were opened.
4. Why do we cluster data?
When applying a clustering algorithm to data, data scientists and others can acquire crucial insights by seeing which groups (or clusters) the data points fall into.
Key Takeaways
In this article, we have discussed the following topics:
- Clustering
- Different types of clustering
- Applications of clustering
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Happy Coding!
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