CLUSTERING- AN INFLUENTIAL TECHNIQUE OF MACHINE LEARNING
Clustering in machine learning is an unsupervised method which is used for grouping the data. Unsupervised learning means the given data is not labeled. This means that the given data do not have answers. The machine has to make predictions according to that data. We know that in machine learning, the data is given to the machine and it learns from that data for generating the outputs. In unsupervised learning, the machine doesn't know which data would give what output. The machine has to guess.
Clustering in machine learning means grouping the data on the basis of similarities between them. The data which have the same properties or are similar to each other are grouped together. It is not compulsory that the data should be exactly the same. The data can have relevant properties. This is known as clustering.
WHY CLUSTERING IS USED IN MACHINE LEARNING?
The clustering process is very influential in unsupervised machine learning. Though it is helpful in supervised machine learning as well. It is easy to group data, which is labeled, but it comes quite difficult to group the unlabeled data. That's why the clustering process is used in machine learning technology for recognizing and extracting useful information from the given data.
There are many algorithms which are used to execute the clustering process. I have listed two of them:
· Hierarchical clustering
In hierarchical clustering, the data which is given as input forms their own cluster. This means each data makes its own cluster. After the formation of the clusters, the two nearest data clusters are merged together to form a single cluster. As the name of the algorithm indicates, it forms a hierarchy by merging two clusters again and again.
In K-means clustering the data which have similarities are grouped together. After such grouping, the data which is left with irrelevant properties are grouped with those clusters which are nearer to them. This is known as K-means clustering.
METHODS OF CLUSTERING
Here are some different ways in which clustering of the data is done.
DENSITY-BASED METHOD:
In the density-based method, the data is grouped together on the basis of the density. The clusters are made of that area which is dense in the graph. They tend to have similar properties. The area which is less dense has different properties from the denser one. This method gives accurate results.
PARTITIONING METHODS
In the partitioning method, the data is divided into n number of clusters. Each partition forms a cluster. For example--> clustering large applications based upon randomized search (CLARANS), k-means, etc.
GRID-BASED METHODS
In grid-based methods, space which is available between the data is changed to the cells. These cells form a grid structure. The cells are finite in number. The speed of processing operations of these grids is independent and faster. For example--> clustering in the queue, wave cluster, statistical information grid (STING).
CONCLUSION
The clustering technique plays an influential role in machine learning technology. Those who are interested in learning more fascinating facts about the clustering in machine learning can enroll themselves here for Machine learning course.
