One of the many popular Machine Learning models, a Clustering Algorithm refers to putting together datasets in a group that resemble each other. The concept of clustering is based on the placing of similar data inputs into a common group and dissimilar or different data inputs into another group.
Homogeneity plays a crucial role in clustering as the algorithms learn to identify similar grounds in datasets that are provided to the machines. An unsupervised learning neural network ML model, Clustering Algorithms deals with unlabelled data that is required to be organized by computers.
While the process of labeling datasets with similar attributes in common groups is known as clustering, clustering algorithms are the methods to perform clustering. For instance, statistical data analysis implements the technology of Clustering Algorithms for data analysis, data interpretation, and other data-related operations.
Under the umbrella concept of Machine Learning, there are both supervised and unsupervised learning models. Further, unsupervised learning models focus on clustering, which we will be learning about in this blog.
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Clustering works by segregating data points into different groups based on the similarity of attributes. For any concept that is novel to human understanding, clustering or grouping elements based on their likeness is important.
Likewise in data science and machine learning, clustering algorithms carry out the task of labeling unlabelled data inputs which further helps in data interpretation and establishing patterns for predictive purposes.
Even though there are numerous types of clustering algorithms that prevail in Machine Learning, we will be understanding the working of Clustering Algorithms with the help of the K-Means Clustering Algorithm .
The simplest unsupervised clustering algorithm, K-Means Clustering Algorithm in Machine Learning, is primarily concerned with grouping unlabelled datasets in different clusters by identifying similar attributes among them.
Herein, K refers to the number of clusters that are required to be created for grouping data inputs. As this algorithm is a centroid-based clustering algorithm, each data cluster is attached to a centroid.
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As it is necessary for the number of clusters to be predefined, the K-Means algorithm majorly focuses on placing data points to their nearest cluster and realigning clusters to get the best results.
Here is a brief account of the K-means algorithm steps which help in better understanding of the concept.
Step i- Identify the number of K to decide the number of clusters to be generated using the Elbow method.
Step ii- Place K points of centroids at random places.
Step iii- Allot each data input to its nearest centroid, which will indicate the predetermined value of k.
Step iv- Compute the variance of each cluster (fluctuation of data inputs) and place a new centroid for each cluster.
Step v- Repeat the first 3 steps to get the best results as there might occur fluctuations or errors while allotting data points to their cluster centroids.
Step vi- Compute the variance after each step to ensure that minimum errors take place.
Step vii- The K-Means clustering model is ready to be used for more data points.
K-Means Clustering Algorithm, Source
As we have already been through the working of the Clustering Algorithms, let us now learn about the different types of Clustering Algorithms. Here we go!
Types of Clustering Algorithms
The first and foremost clustering algorithm, Centroid-based algorithm, is a non-hierarchical structure that allows data analysts to group data points in different clusters according to their attributes of characteristics.
As the name suggests, these algorithms structure a specific cluster around a centroid or a central point that directs the allotment of data points. Such algorithms are inclusive of outliers or those data inputs that are placed far off from others.
The most popular centroid-based algorithm is the K-Means Algorithm which has already been discussed in the previous segment. Herein, the number of clusters has to be defined beforehand, and the data points are ‘partitioned’ from each other. Perhaps this is the reason why Centroid-based algorithms are also known as Partitioning Algorithms.
These algorithms combine data inputs with high density into one cluster. Density-based algorithms look after the density of data inputs in a plot and thereby allocate them to clusters based on their proximity to each other.
This method is not inclusive of outliers or distant data points since they do not belong to an area of high density. An efficient method of clustering, density-based algorithms differentiate between data inputs that have different densities.
However, when data points are relatively scattered and dimensionally distant, such algorithms might get inefficient in clustering them. Perhaps density-based algorithms are better off for unlabelled data inputs that are placed close to each other.
Distribution-based algorithms focus on grouping distinct data points based on their source of distribution. This is done by referring to Gaussian distributions.
(Also read: Top 10 Statistical Data Distribution Models)
Generally, the number of clusters formed is on the basis of normal distribution sources found in a plot. For those data points that seem to be distant from either of the sources, the algorithm leaves them behind.
Perhaps it is preferable to use other clustering algorithms if the source of distribution of data points is unclear or unknown. This particular method closely deals with statistics as data points are studied under this method in order to trace their point of generation or origination.
Hierarchical clustering algorithms, unlike centroid-based algorithms, take a different turn when it comes to clustering.
These algorithms focus on constructing a hierarchy among all data points and from there on, generate a mind map that outlays the relation between all data inputs.
For instance, taxonomies can be used for understanding hierarchical clustering algorithms that help us to find a hierarchy and consequent relations among various data points.
Either the clustering of data points takes place through the agglomerative clustering approach (bottom to top) or through the divisive hierarchical clustering approach (top to bottom).
At first, each data point is treated as a separate cluster that is then studied along with other data points. Subsequently, data points are merged with each other based on their hierarchy and distinct clusters are formed in a tree-like arrangement.
A constraint-based Algorithm or Supervised Algorithm is a method of creating desired clusters on the basis of size or number of items.
Unlike other algorithms that are unsupervised clustering algorithms, this particular method of clustering is given a training dataset that helps it to work on new data points.
So, when new data inputs are fed to machines, clusters of desired traits are formed that might even be concerned with certain traits of data points. Under the constraint-based method in data mining, the machine is first prepared for supervised clustering, and then it is given data inputs to work upon.
When new data inputs are provided, the clustering algorithm partitions the desired data inputs into a cluster and thereby produces the wanted results.
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Quite distinct from other methods of clustering, the Fuzzy Clustering Algorithm creates clusters of data points in such a manner that one data point can belong to more than one cluster.
Based on the notion that some data inputs can overlap in terms of characteristics, this algorithm places a particular data input in more than one cluster according to the parameters of different clusters.
One such method is the Fuzzy C-Means Clustering Algorithm wherein each data point is placed in more than one cluster and it is allotted with a different membership degree depending on the distance between the data point and the center of the cluster.
The closer the data point is to the cluster center, the higher its membership to that specific cluster is.
(Related blog: Fuzzy Logic Approach in Decision Making)
After learning so much about Clustering Algorithms, let us now look at the most common applications of the Machine Learning model.
The Marketing and Sales Department have been successful in using Clustering Algorithms. These methods of clustering help marketing professionals to narrow down data points in various clusters that help them to segregate their audiences on various grounds - sociological, demographics, etc.
You read it right! Clustering Algorithms from Machine Learning have been very useful in the detection of fake news and stories. As these algorithms help to determine words or phrases that are often used in click bait content or spam messages, professionals can quickly identify which news is fake and which is true.
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The segregation of documents is another application of Clustering Algorithms wherein information is used to form clusters and with the help of that various documents can be segregated in a short period of time.
In large-scale organizations where document segregation can be tiring, clustering algorithms can help professionals to get done with this task in almost no time!
Data Analytics and Machine Learning go a long way. Yet, the unbeatable help that Clustering Algorithms provides is over the top. In the field of data analytics, clustering methods allow big bits of data to be grouped into clusters based on their similarities and dissimilarities. This can further help in data interpretation and data storage as well.
The last of all yet the most important one is the use of clustering algorithms in search engines. Ever thought about how quickly a search engine displays the result as per your search?
Well, it is all about clustering. Clustering algorithms assist search engines in grouping traits of web pages with each other which, in turn, helps them to rapidly provide results to users from all around the world.
Grouping of data inputs on the basis of commonalities is known as clustering. The process of clustering is carried out by clustering algorithms that are of various types.
An unsupervised Machine Learning model, Clustering Algorithms are very helpful in our day-to-day lives and have the ability to perform human functions in a very short span of time.
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