K-Means Clustering vs. Hierarchical Clustering
Understanding Clustering Techniques
In Machine Learning, clustering is a primary technique used to group datasets in a meaningful way. The primary objective of clustering is to explore patterns in the data, and it helps the Machine Learning model in identifying similarities and differences between different categories more efficiently. However, there are different clustering algorithms, each with its methodology and working principle. Two commonly used clustering algorithms are K-Means Clustering and Hierarchical Clustering. But what makes them different from each other? In this article, we will explore the differences between these two algorithms and their applications in the real-world.
K-Means Clustering
K-Means Clustering is an unsupervised Machine Learning algorithm that aims to partition datasets into K groups based on their similarities and differences. It accomplishes this by first, placing the points randomly in K-clusters, and then locating the nearest centroid. Next, it assigns the points to their nearest centroid, such that the distance to the cluster center is minimized. However, the K-Means algorithm has some assumptions that make it perform well only in certain conditions. The first one is that the K value must be fixed ahead of time. Secondly, it assumes that the data has equal variance and that the attributes are evenly distributed across the data. Furthermore, the K-Means algorithm could be challenging to implement for high dimensional datasets, and it might converge to local optimums rather than the global optimum. Want to learn more about the subject covered? Check out this useful content, check out the carefully selected external content to supplement your reading and enhance your knowledge of the topic.
Hierarchical Clustering
Hierarchical Clustering is also an unsupervised Machine Learning algorithm that aims to create a hierarchy of clusters based on their similarities and differences. It uses a dendrogram, which is a branching diagram that illustrates how different clusters are nested as it merges one point at a time. Hierarchical Clustering has two approaches: Agglomerative and Divisive. Agglomerative clustering starts with every observation as a separate cluster and merges the most similar clusters until all the data is clustered into a single group. In contrast, Divisive clustering begins with a single cluster that contains all the data and splits it into multiple subsets based on their differences.
Comparing K-Means Clustering and Hierarchical Clustering
The primary difference between K-Means Clustering and Hierarchical Clustering is how they partition the datasets. K-Means groups the datasets into a pre-set number (K) of clusters, whereas Hierarchical Clustering creates a dendrogram of nested clusters. Hierarchical clustering is more flexible and can handle different types of data and dissimilar data sizes better. However, it can be more complex and computationally expensive, making it less practical for larger datasets. In contrast, K-Means is more scalable and faster, and due to its simplicity, it can be used for highly dimensional datasets. However, K-Means is not very robust, and the clusters could vary significantly by the initial centroid positions.
Applications of K-Means Clustering and Hierarchical Clustering
The real-world applications of clustering include image segmentation, market segmentation, social network analysis, and anomaly detection. K-Means Clustering can be used for market segmentation and pattern recognition as it can assign the new data points to the nearest centroid. K-Means is also used in image segmentation to partition the images into different parts that are easier to handle. In contrast, Hierarchical Clustering is used for anomaly detection, where it can detect an outlier in highly dimensional data. It is also used for social network analysis to group people who have shared interests or ac,tivity, and identifies the main influencers in the network. Looking to deepen your knowledge on the subject? Check out this external resource we’ve prepared for you, offering additional and relevant information to expand your comprehension of the topic. Click to learn more on this subject!
Conclusion
In conclusion, K-Means Clustering and Hierarchical Clustering are two popular unsupervised Machine Learning algorithms for clustering data. While both methods achieve the same goal of grouping data into meaningful shapes, they follow different approaches. K-Means Clustering divides the data into pre-defined K clusters using centroids, whereas Hierarchical Clustering creates nested clusters based on similarities and dissimilarities between data. Both techniques have different advantages and disadvantages, and the choice between them depends on the type, size, and objectives of the data used.
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