Introduction to Clustering Algorithms
Clustering Algorithm is a type of Machine learning algorithm that is useful for segregating the data set based upon individual groups and the business need. It is a popular category of Machine learning algorithm that is implemented in data science and artificial intelligence (AI). There are two types of clustering algorithms based upon the logical grouping pattern such as hard clustering and soft clustering. Some of the popular clustering methods based upon the computation process are K-Means clustering, connectivity models, centroid models, distribution models, density models, hierarchical clustering. The use cases for clustering algorithms are image segmentation, market segmentation, and social network analysis.
Types of Clustering Algorithm
Basically, the clustering algorithm is subdivided into two subgroups which are:
1. Hard Clustering: In hard clustering, a group of similar data entities belongs to a similar trait or cluster completely. If the data entities are not similar up to a certain condition, the data entity is completely removed from the cluster set.
2. Soft Clustering: In soft clustering, relaxation is given to every data entity which finds a similar like-hood data entity to form a cluster. In this kind of clustering, a unique data entity can be found in multiple clusters set according to their like-hood.
What is Clustering Methodology?
Every clustering methodology follows a set of rules which define their set of similarity between data entity. There are hundreds of clustering methodologies available in the market today. So let’s take some of it into consideration which is very popular nowadays:
1. Connectivity Models
As clearer by its title, in this mechanism algorithm find the nearest similar data entity in the group of set data entities based on the notion that the data points are closer in data space. So the data entity nearer to the similar data entity will exhibit more similarity than the data entity lying very far away. This mechanism also has two approaches.
In the first approach, the algorithm starts dividing a set of data entities in a separate cluster and then arrange them according to the distance criteria.
In another approach, the algorithm subset all the data entity into a particular cluster and then aggregate them according to the distance criteria as the distance function is a subjective choice based on user criteria.
2. Centroid Models
In this type of iterative algorithm, a certain centroid point is taken into consideration first, then the similar data entity according to their closeness relative to this centroid point is set into a cluster. The most popular K-Means Clustering algorithm was not successful in this type of clustering algorithm. One more note is that no clusters are predefined in centroid models so we have an analysis of the output data set.
3. Distribution Models
In this type of algorithm, the method finds that how much is it possible that each data entity in a cluster belongs to identical or same distribution like Gaussian or normal. One drawback of this type of algorithm is that in this type of clustering, the data set entity has to suffer from overfitting.
4. Density Models
Using this algorithm, the data set is isolated in respect to different density regions of data in the data space and then the data entity is assigned with specific clusters.
5. K Means Clustering
This type of clustering is used to find a local maximum after each iteration in the set of multiple data entity set. This mechanism involves 5 steps mentioned below:
- First, we have to define the desired number of the cluster we want in this algorithm.
- Each data point is assigned to a cluster randomly.
- Then we have to calculate centroid models in it.
- After this, the relative data entity is re-assigned to its nearest or closest clusters.
- Re-arrange cluster centroid.
- Repeat previously two steps until we get the desired output.
6. Hierarchical Clustering
This type of algorithm is similar to the k-means clustering algorithm, but there is a minute difference between them which are:
- K- means is linear whereas hierarchical clustering is quadratic.
- Results are reproducible in Hierarchical clustering unlikely to k-means which gives multiple results when an algorithm is called multiple times.
- Hierarchical clustering works for every shape.
- You can interrupt the Hierarchical clustering anytime when you get the desired result.
Applications of Clustering Algorithm
Now it’s time to know about the applications of the clustering algorithm. It has a very vast feature incorporated in it. A clustering algorithm is used at a various domain which are
- It is used in Anomaly detection
- It is used in Image segmentation
- It is used in Medical imaging
- It is used in Search result grouping
- It is used in Social network analysis
- It is used in Market Segmentation
- It is used in Recommendation engines
A clustering algorithm is a revolutionized approach to machine learning. It can be used to upgrade the accuracy of the supervised machine learning algorithm. We can use these clustered data entities in various machine learning algorithms to get high accuracy supervised results. It is accurate that IT can be used in multiple machine learning tasks.
So it has a large number of applications in various domains such as mapping, customer reports, etc. Using clustering we can easily increase the accuracy of the machine learning approach. So taking future aspects into consideration, I can say that this algorithm is used almost in every technology in the field of software development. So anyone interested in pursuing its career in machine learning, need to know deep about the clustering algorithm as it is directly related to machine learning and data science. Apart from that, it is good to have the technique needed in every technology, so it can always return a good approach.
This has been a guide to the Clustering Algorithm. Here we have discussed the introduction to the Clustering Algorithm along with its types, methodology, and its applications. You may also look at the following article to learn more –