Machi9ne learning is the backbone of the data sciences. There are two basic types of machine learning. One is supervised learning, and the other is unsupervised learning. In supervised learning, the training data is labeled, and the machine learning model is trained over the labeled data set. While in unsupervised learning, the data set features don’t have any class labels—the machine learning algorithms train data sets without using any class labels. There are several algorithms in both supervised and unsupervised machine learning. While talking about unsupervised machine learning algorithms, there are very prominent algorithms called clustering algorithms. In clustering, the data records or objects of the data set are grouped based on similarity in them. For more details Data Science Course in Pune
What is Clustering:
In clustering, the data given data set is not labeled. The clustering algorithm combines the given data into several groups of similar data objects. There are many clustering types, and several machine learning algorithms are used for each type of clustering. We will discuss some of the clustering algorithms here.
Types Of Clustering:
Here we have discussed some of the clustering types:
In hard clustering, each item of the data set is assigned one group or a cluster. For example, if we want to divide the team’s 11 players into clusters, there will be 11 clusters for each of the data items.
In this type of clustering, each data item is not put into a separate cluster. Instead of doing this, data sets are put into different groups or clusters based on the likelihood or probability. The probability is calculated for every data item to fall in a particular data set. Machine learning algorithms automatically calculate the probability and put a certain data item into a specific cluster or group.
Connectivity Clustering Algorithms:
These clustering algorithms place all the data items in one group or cluster in the first step. In the next step, some of the data items are placed in two or three groups based on the similarity. For similarity, the distance between the data items is determined. The data items with a minimum distance from other data items are placed in one cluster or group. In the next step, again, this strategy is applied, and more clusters are formed. In this type, there are two ways to build clusters. One way is top-down, in which data items are placed in one cluster in the first iteration, and in the next iterations, different clusters are formed. The other way is the bottom-up approach, in which each data item is placed in a separate group in the first iteration. In the next step the number of clusters is reduced, and similar data items are grouped. Thus, in the end, the resultant is the one cluster. Both the approaches are used according to the need and requirements of the scenario and data set. Learn more about Data Science Course in Chennai
We have discussed the prominent types and techniques of clustering. For more articles related to data sciences, please keep visiting our blog.