Top 7 Clustering Algorithms Data Scientists Should Know

• Last Updated : 04 Jan, 2022

Clustering is primarily concerned with the process of grouping data points based on various similarities or dissimilarities between them. It is widely used in Machine Learning and Data Science and is often considered as a type of unsupervised learning method. Subsequently, there are various standard Clustering algorithms out there that are being utilized to group these data points. As per the clustering requirements, clusters formed from the input data points are segregated and here, begins the main game which data scientists need to play. This is because now, they need to be selective with any of the clustering algorithms so that the available datasets can be represented well in the form of clusters

Meanwhile, if you feel like becoming an aspiring data scientist or grabbing any position well-renowned in the market of Data Science, then you must give a glance at the top clustering algorithms. Here, in this article, we’re going to discuss the top 7 Clustering Algorithms that all the budding Data Scientists should know:

1. Agglomerative Hierarchical Clustering

Hierarchical Clustering is common in our day-to-day lives and we most often neglect it when it produces a nested sequence of clusters. Such clusters are arranged by either a top-down or bottom-up approach. Top-down means viewing the datasets from source to its general subsets just like father, children, and grandchildren while the bottom-up lets us view datasets from the general ones to the source. Indeed, the bottom-up approach is nothing but Agglomerative Hierarchical Clustering in which various data points are clustered as multiple data pairs.

Then, these data pairs obtained which are obviously clusters are merged till a big singleton cluster consisting of all data points is obtained. Thinking about the name of the tree-like visual representation of this type of hierarchical clustering? Its name is dendrogram. Let’s implement now the listed below steps successfully of the Agglomerative Hierarchical Clustering

Algorithm:

• Each data point is a cluster and let’s assume that the total number of clusters is m.
• Now, setting up the proximity/distance matrix of m*n is all you need to do keeping in mind mapping the distance between the two data points participating in forming a cluster.
• Meanwhile, you will find one or some data pairs with common similarities. Use that pair which is much similar to other ones already existing and then, keep on updating the distance matrix.
• To measure the distance between the endpoints well, you can use any of the techniques – single-link, centroid, complete-link, and average-link.
• Keep on updating the matrix through any of the distance-measuring techniques mentioned in the above step till you reach the source where a singleton cluster consisting of all objects is left.

2. Balanced Iterative Reducing & Clustering

Known in the market as BIRCH, Balanced Iterative Reducing & Clustering using Hierarchies is one of the best-unsupervised clustering algorithms. This is a four-phase algorithm efficiently imbibing useful data identification patterns with the help of appropriate hierarchies so that larger databases comprising of multi-dimensional data points can be managed without any compromises on the quality of clusters.

Thinking if the algorithm is bound to the constraints like time and memory? Without any hesitation, there are constraints adhered to by this algorithm but still, it has the potential of finding the best quality clusters through a single database scan. Take a look at the four phases of BIRCH explained briefly:

• First Phase: This is one of the most important phases and begins with the thought of creating a CF or Clustering Feature tree. Here, in this phase, there are a few steps and the first one is:
1. CF is represented as a three-dimensional vector in this form CF = (N, LS, SS). N is the number of instances/data points selected, LS is their linear sum, while SS is the square sum of N.
2. There will be many CFs like the above steps which will iteratively be represented as hierarchically balanced by a tree named CF tree. You will find two parameters of this tree:
• Branching Factor (to estimate the maximum number of children a leaf node can accommodate)
• Threshold (the maximum diameter value amongst data points within a sub-cluster present at leaf nodes). Furthermore, there are other parameters like T (size of CF tree) and P (size of the page where it is required to fit the leaf or non-leaf node).
3. Now, you might be thinking about how this CF tree is represented hierarchically? For this, non-leaf nodes are exhibited as [CF_{i}, child_{i}], where [child_{i}] is a pointer pointing towards its ith child node while [CF_{i}] is the clustering feature representing well the sub-cluster associated.
4. At the end of this phase, a CF tree is created well so that we may now jump to another phase which is scanning the CF tree.
• Second Phase: This phase can be pronounced as the data condensing or CF tree resizing phase. Though it is marked optional as per the BIRCH’s original presentation, yet it holds prime importance since through this phase, the represented CF tree can be rebuilt into a smaller one by:
1. Group densely crowded sub-clusters into a larger cluster comprising of multiple data points stored in the tree as nodes (leaf or non-leaf).
2. Removing abnormal diameter values so that data condensation can be carried forward smoothly.
• Third Phase: This phase is another name of Global Clustering. Here, any of the existing clustering algorithms like K-Means, etc. are applied for clustering well all the leaf-node entries lying within the CF tree. Reason for applying any of the globally present recent Clustering algorithms? Any of those will let you flexibly specify both – number of clusters desired and the diameter threshold essential for quality clustering.
• Fourth Phase: Last or the fourth phase, also pronounced as cluster refining. After the sets of clusters are obtained from the third phase, they are further filtered out in a redistributed manner into seeds (as per the centroid principle) so that a better version of clusters is obtained handling well databases with larger datasets. And at the end, repetitive or abnormal leaf-or-non-leaf values are identified and removed for loading better clusters into the memory.

3. EM Clustering

Known in the field of data science as a solution that can conquer well the drawbacks of K-Means, EM or Expectation Maximization clustering algorithm uses Gaussian function for intuitively estimating the missing values from the available datasets. Then, restrictively, it shapes the clusters through optimized values of mean and standard deviation.

The whole process of estimation and optimization is carried forward till the point a single cluster is obtained resembling well with the likelihood of outcomes. Let’s now know a bit about the steps of the EM Clustering algorithm:

• Considering a set of parameters with the likelihood of randomness in the observations recorded. The prime purpose of selecting the variables randomly is quickly accessing many data clusters onto which estimation and maximization events will be performed.
• This is the next step known as Estimation. Here, data clusters formed are observed in a way that the values missing can be estimated through a probability distribution function (any of the Gaussian mixture models present such distribution keeping in mind the maximum likelihood of estimated values).
• Now, it’s time to perform the optimization technique via probability distribution function by computing parameters like mean and standard deviation of the datasets likely to be much closer to a selected cluster.
• At last, convergence which is a plain-programming method is given attention and the condition is met after steps 2, 3 are performed iteratively. The datasets used in estimation and optimization or maximization steps are probabilistically cross-checked to a point the difference between probabilities of the likelihood of their occurrences is negligible or almost zero. In case if required, we may repeat the calculations of estimated and expected values till the point of convergence meets in actuality. As soon as this point is identified, the algorithm (working on the use-observe-update principle) can be put to a halt and one can enjoy the accurate results promisingly removing all the inconsistencies.

4. Hierarchical Clustering

Hierarchical Clustering algorithm works like magic at times you are on a mission of identifying the data elements and mapping them in accordance with the likelihood of clusters. Now, the data elements mapped after comparison may belong to a cluster whose properties are different in terms of multidimensional scaling, cross-tabbing, or quantitative relationship among data variables on multiple factors.

Thinking about how to identify a single cluster after merging the available clusters keeping in mind the hierarchy of their features on the basis of which they are classified? For doing this, the steps of the Hierarchical Clustering algorithm written below can be given a look:

• Start with selecting the data points and map them as clusters in accordance with the hierarchy.
• Thinking about how the clusters will be interpreted? Here, a dendrogram can be used for interpreting well the hierarchy of clusters with a top-down or bottom-up approach.
• Clusters mapped are merged till a point a single cluster is left and for measuring the closeness between the clusters while merging them, we may use multiple metrics like Euclidean distance, Manhattan distance, or Mahalanobis distance.
• The algorithm is terminated for now since the intersection point is identified and mapped well on the dendrogram.

5. Density-Based Spatial Clustering

Density-based Spatial Clustering Algorithm with Noise (or DBSCAN) is a better choice than K Means when it comes to identifying clusters, just by cross-examining the density of its data points, in larger spatial databases. Also, it is attractive and 100 times efficient than CLARANS i.e. Clustering LARge ApplicatioNS via Medoid-based partitioning method. Because of its density-based notion of identifying clusters, it was awarded for receiving substantial attention in terms of both practice and theory.

Meditating what basic concept this algorithm uses? So, this award-winning spatial data clustering algorithm selects an arbitrary point and then, identifies other points which are near to the arbitrary one. Later, the data points recognized with the help of an arbitrary one are recognized as a cluster and the one far away from the arbitrary point (named as noise/outliers) are used in other iterations of identifying the clusters. Let’s know more clearly about the steps of this awarded algorithm:

• Begin with considering a large spatial database for discovering the clusters of arbitrary shapes. Within that space, we select an arbitrary point say p and then, proceed ahead with finding its nearest neighborhood data point like q via distance parameter ε.
• More data points (like q) can now be identified till a stage where a cluster of arbitrary shape and density is approximately identified. The number of those data points will come into the picture since their clustering has started with some value say 5 or more of minPts (minimum points required to form a density-based cluster). (Note: All points of a cluster are mutually densely connected. A point selected is a part of a cluster if it is densely reachable with some already existing point.)
• This is quite possible that not reachable points are reviewed during the clustering process. Instead of discarding them, they can be symbolized as noise/ outliers.
• Prefer repeating the above steps i.e. 2 & 3 so that the data points examined can become a part of the cluster having some shape and density and later, those labeled as noise will be visited later.
• In the end, the noise or outliers shall be visited for identifying their neighbor data points somewhere forming clusters that are lying in low-density regions. (Note: It is not mandatory to traverse outliers as they are visible in low-density regions.)

6. K-Means Clustering

K-Means Clustering Algorithm iteratively identifies the k number of clusters after computing the centroid value between a pair of data points. With its vector quantization observations, it is pretty advantageous to compute cluster centroids by virtue of which data points of variable features can be introduced to clustering.

And as the clustering process speeds up, a lot of real-world data emerging as unlabeled will now be comparatively efficient since it is now segmented into clusters varying in shape and density. Thinking about how the centroid distance is calculated? Take a look at the listed-below k means steps:

• Select at first the number of clusters that may vary in shape and density. Let’s name that number k whose value you can choose like 3,4, or any other.
• Now, you may assign data points to the cluster’s number. Then, with the data point and cluster selected, the centroid distance is computed through the least squared Euclidean distance.
• If the data point is much closer to the centroid distance, then it resembles the cluster otherwise not.
• Keep computing the centroid distances iteratively with the selected data point till you identify a maximum number of clusters comprising of similar data points. The algorithm stops its clustering process as soon as guaranteed convergence (a point where data points are clustered well) is achieved.

7. Ordering Points To Identify the Structure of Clustering

OPTICS or Ordering Points To Identify the Structure of the Clustering Algorithm has the potential of improving database cataloging. You may ponder what actually is database cataloging!! So, database cataloging is a way of sequentially arranging the list of databases comprising of datasets residing within the clusters.

These clusters are of variable densities and shapes and hence, their structure varies. Furthermore, the basic approach of OPTICS is similar to that of the Density-based Spatial Clustering Algorithm (already discussed in point number 5) but at the same time, many of the DBSCAN’s weaknesses are addressed are meaningfully resolved.

The prime reason for detecting and resolving the DBSCAN’s weaknesses is that now, you need not worry about the identification of more densely populated clusters which wasn’t done by DBSCAN. Wanna see how this algorithm works? Just read these below-steps:

• Primitively, a set of unclassified data points can be reviewed as now there is no need for specifying the number of clusters. Then, you should select some arbitrary point like p and start computing the distance parameter ε for finding the neighborhood point.
• To proceed ahead with the clustering process, it is essential to find the minimum number of data points with which a densely-populated cluster can be formed. And that number can be denoted by variable minPts. Here, the process may stop if the new data point identified is greater than minPts.
• Keep on updating the values of ε and the current data point till the clusters of different densities are segmented well even better than DBSCAN.

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