K-means clustering is a prototype-based, unsupervised and iterative machine learning method. It uses to find clusters of data items in a dataset. Here all data points gather into k numbers of groups. Where every group is described by its prototype. There are many clustering algorithms available, but k-means is one of the most popular. These characteristics make k means clustering in Python relatively simple to implement. This is especially for beginner programmers and data analysts.
To give you a more clear detail about clustering, we will discuss some major points. Here we will give some details of clustering and its types. Later we will discuss K means clustering in Python.
What is clustering?
It is a collection of methods for dividing data into clusters or groups. Clusters are roughly described as collections of data. It relates more to each other than to data objects from other groups.
In practice, clustering assists in the identification of two types of data:
- Meaningful clusters
- Usefulness clusters
Domain knowledge is developed with the help of meaningful clusters. In the medical profession, for instance, clustering was used in gene expression tests. The clustering results revealed groupings of people who respond to medical treatments differently.
Useful clusters, whereas, function as a data pipeline’s intermediary phase. Businesses, for instance, utilize clustering to divide their customers. Customers separate into segments or groups with similar purchase histories. This is as a consequence of the clustering results. The companies can utilize it to construct targeted marketing campaigns.
What are the different types of clustering methods?
You can perform clustering using many different approaches. Each of these groups has its own set of pros and cons. This means that various clustering methods will provide more natural cluster assignments.
- Clustering by partition (or Partitional Clustering)
- The clustering in a hierarchy (or Hierarchical Clustering)
- Clustering based on density (or Density-Based Clustering)
Before diving into k means clustering in Python, it’s good to study these categories. Moreover, you’ll explore the pros and cons of each category. It will help you better understand how k-means work.
Partitional Clustering
Data items are separated into non – overlapped groups using partitional clustering. To put it another way, no item can belong to more than one group. And each group must include at least one item. The number of clusters, denoted by the variable k, must be specified by the user in these methods.
K-means and k-medoids are two examples of partitional clustering techniques. These algorithms are both non-deterministic. It means that even if the input is the same, two successive runs might generate varying outcomes.
The following are some of the pros of partitional clustering methods:
- They work perfectly with spherical shape clusters.
- In terms of algorithm complexity, they’re extensible.
The following is the basic cons of partitional clustering methods:
- They also have a drawback. They aren’t well suited to clusters with complicated shapes and varying sizes.
Hierarchical Clustering
These types of clustering define group tasks by building an order. This executes by either a top-down or bottom-up approach:
Agglomerative clustering
- As a bottom-up approach. It connects the two points. These are similar until all points have been combined into a single group (cluster).
Divisive clustering
- As a top-down approach. It begins with all points as one group. And breaks the smallest similar clusters at each step until only one data point remains.
These techniques create a tree-based structure of points recognized as a dendrogram. It works the same as partitional clustering. The number of clusters (k) in hierarchical clustering frequently chosen by the user. Moreover, clusters create by splitting the dendrogram at a certain depth. It is resulting in k means clustering in Python of shorter dendrograms.
Unlike several partitional clustering methods, hierarchical clustering is a deterministic process. Also, it means that cluster assignments will not vary. Moreover, it is possible if the algorithm performs repeatedly on the same input data.
The pros of hierarchical clustering techniques contain the following:
- They usually display the finer facts about the connections between data objects.
- They give an interpretable dendrogram.
The cons of hierarchical clustering techniques contain the following:
- They are computationally costly with respect to algorithm difficulty.
- They are sensitive to outliers and noise.
Density-Based Clustering
Cluster assignments determine using density-based clustering. It depends on the data points density in an area. Clusters allocated where there is a large density of data points. And each divided by a low-density area. Also, this method does not need the user to provide several clusters. That is unlike the other clustering methods. Instead, there is an adjustable threshold based on a distance-based parameter. This threshold defines how near points must be in order for them to be regarded as a cluster member.
Examples of density-based clustering methods:
- Applications of Density-Based Clustering with DBSCAN or
- Noise, and Ordering Points to determine the clustering architecture.
The pros of these clustering techniques include the following:
- They are best at determining clusters of nonspherical shapes.
- They are resistant to outliers.
The cons of these clustering techniques include the following:
- They do not suit clustering in high-dimensional spaces.
- They have a problem recognizing clusters of contrasting densities.
Let’s wrap it up!
We hope you are clear with the K means clustering in Python. We have discussed different types of clusters along with their pros and cons. Moreover, if you still have any doubts with K means clustering in Python, let us know through your mails. We will provide you with the relevant solution to your query(s). Thank you for putting your precious time into reading this blog.
