What is the clustering method in ML?
Clustering or cluster analysis is a machine learning technique, which groups the unlabelled dataset. It can be defined as “A way of grouping the data points into different clusters, consisting of similar data points. The objects with the possible similarities remain in a group that has less or no similarities with another group.”
The clustering is done by finding similar patterns in the unlabelled dataset such as shape, size, color, behavior, etc., and divides them as per the presence and absence of similar patterns among them.
It is an unsupervised learning method because no supervision is provided to the algorithms hence it deals with unlabeled datasets.
After applying the clustering technique, cluster-ID is provided to each cluster or group. ML system will be using this id to simplify the processing of large and complex datasets.
Example: Let’s understand the clustering technique with the real-world example of Mall: When we visit a shopping mall, we can observe that the things with similar usage are grouped at one side. Such as the t-shirts are grouped in one section, and the pants are in other sections, similarly, in vegetable sections also, apples, bananas, Mangoes, etc., are grouped in separate sections, so that we can easily find out the things. The clustering technique also works in the same way. Other examples of clustering are grouping documents according to the topic.
The below diagram explains the working of the clustering algorithm. We can see the different fruits are divided into several groups with similar properties.
Types of algorithms in clustering
- K-means algorithms
- Mean-shift algorithms
- DBSCAN algorithms
- Expectation-Maximization Clustering using GMM
- Agglomerative Hierarchical algorithm
- Affinity propagation
1) K-means algorithms: The k-means algorithm is one of the most popular clustering algorithms. It classifies the dataset by dividing the samples into different clusters of equal variances. The number of clusters must be specified in this algorithm. It is fast with fewer computations required, with the linear complexity of O(n).
2) Mean-shift algorithm: Mean-shift algorithm tries to find the dense areas in the smooth density of data points. It is an example of a centroid-based model, that works on updating the candidates for centroid to be the center of the points within a given region.
3) DBSCAN Algorithm: It stands for Density-Based Spatial Clustering of Applications with Noise. It is an example of a density-based model similar to the mean-shift, but with some remarkable advantages. In this algorithm, the areas of high density are separated by the areas of low density. Because of this, the clusters can be found in any arbitrary shape.
4) Expectation-Maximization Clustering using GMM: This algorithm can be used as an alternative for the k-means algorithm or for those cases where K-means can be failed. In GMM, it is assumed that the data points are Gaussian distributed.
5) Agglomerative Hierarchical algorithm: The Agglomerative hierarchical algorithm performs the bottom-up hierarchical clustering. In this, each data point is treated as a single cluster at the outset and then successively merged. The cluster hierarchy can be represented as a tree structure.
6) Affinity Propagation: It is different from other clustering algorithms as it does not require to specify the number of clusters. In this, each data point sends a message between the pair of data points until convergence. It has O(N2T) time complexity, which is the main drawback of this algorithm.
Real-World Examples of clustering algorithms
1. Identifying Fake News
Fake news is not a new phenomenon, but it is one that is becoming prolific.
What the problem is: Fake news is being created and spread at a rapid rate due to technological innovations such as social media. The issue gained attention recently during the 2016 US presidential campaign. During this campaign, the term Fake News was referenced an unprecedented number of times.
How clustering works: In a paper recently published by two computer science students at the University of California, Riverside, they are using clustering algorithms to identify fake news based on the content.
The way that the algorithm works is by taking in the content of the fake news article, the corpus, examining the words used, and then clustering them. These clusters are what helps the algorithm determine which pieces are genuine and which are fake news. Certain words are found more commonly in sensationalized, click-bait articles. When you see a high percentage of specific terms in an article, it gives a higher probability of the material being fake news.
2. Spam filter
Do you know the junk folder in your email inbox? It is the place where emails have been identified as spam by the algorithm. Many machine learning courses, such as Andrew Ng’s famed Coursera course, use the spam filter as an example of unsupervised learning and clustering.
What the problem is: Spam emails are at best an annoying part of modern-day marketing techniques, and at worst, an example of people phishing for your personal data. To avoid getting these emails in your main inbox, email companies use algorithms. The purpose of these algorithms is to flag an email as spam correctly or not.
How clustering works: K-Means clustering techniques have proven to be an effective way of identifying spam. The way that it works is by looking at the different sections of the email (header, sender, and content). The data is then grouped together.
These groups can then be classified to identify which are spam. Including clustering in the classification process improves the accuracy of the filter to 97%. This is excellent news for people who want to be sure they’re not missing out on your favorite newsletters and offers.
3. Marketing and Sales
Personalization and targeting in marketing is big business. This is achieved by looking at specific characteristics of a person and sharing campaigns with them that have been successful with other similar people.
What the problem is: If you are a business trying to get the best return on your marketing investment, it is crucial that you target people in the right way. If you get it wrong, you risk not making any sales, or worse, damaging your Customer trust.
How clustering works: Clustering algorithms are able to group together people with similar traits and likelihood to purchase. Once you have the groups, you can run tests on each group with different marketing copy that will help you better target your messaging to them in the future.
Why students should learn clustering algorithms
Every student should be familiar with the clustering algorithms because clustering algorithms process, where similarities and dissimilarities of the datasets are made cluster or group. Clustering helps in understanding the natural grouping in a dataset. Their purpose is to make sense to partition the data into some group of logical groupings. Clustering quality depends on the methods and the identification of hidden patterns. Due to these advantages of clustering and the way it is rapidly growing, it is said that it’s the future of ML.
Conclusion focus Clustering is one of the important methods for knowledge discovery and data mining applications. Spatial-temporal data is being generated in large amounts and needs to be analyzed. The paper introduces a new density-based clustering algorithm for clustering spatial-temporal data.