What is Clustering?
Clustering is an unsupervised machine learning technique that involves the grouping of data points or objects in a dataset into different categories based on the similarity of their attributes. It is used in various fields, including marketing, biology, and computer science, to detect patterns and gain insights from large datasets.
K-Means clustering is a popular clustering algorithm that separates a dataset into K number of non-overlapping clusters. K is determined by the number of predefined clusters that are required to separate the data points. The algorithm starts by randomly choosing K initial centroids, which are used to group the data points into different clusters based on the proximity of their attributes to the centroid of the cluster. The algorithm then calculates the centroid of each cluster, and the data points are assigned to the nearest centroid. This process repeats until convergence is reached, and the final clusters are produced.
One of the advantages of K-Means clustering is that it is relatively computationally efficient, making it ideal for large datasets. It can also be used in a wide range of applications, including image segmentation and customer segmentation, to identify similar groups of data points that share similar characteristics.
Hierarchical clustering is another popular clustering algorithm that involves creating a tree-like structure of nested clusters by repeatedly merging the two closest clusters until there is only one cluster left. There are two types of hierarchical clustering: Agglomerative and Divisive.
Agglomerative clustering starts by considering each data point as an individual cluster and then merges them based on their proximity. The algorithm then iteratively combines the clusters until there is only one cluster left.
Divisive clustering or Top-Down clustering, on the other hand, works the opposite of the Agglomerative clustering algorithm. It starts by considering all the data points as one big cluster, and then recursively splits them until each data point is in its own cluster.
Differences between K-Means and Hierarchical Clustering
Although both K-Means and Hierarchical clustering are popular clustering algorithms, there are significant differences between them.
K-Means is a partitional clustering algorithm, which means it requires pre-specification of the number of clusters. On the other hand, hierarchical clustering is a hierarchical algorithm that does not require knowledge of the number of clusters. It can also be used to identify sub-clusters within a larger cluster.
K-Means clustering is faster and more suited for large datasets than hierarchical clustering, as it has lower time complexity. However, hierarchical clustering produces a more understandable and interpretable dendrogram, which can provide more insights into the data structure and relationships between the clusters.
Another significant difference between the two is the determination of the number of clusters. In K-Means clustering, the number of clusters is determined by the user, while in hierarchical clustering, the number of clusters is determined by the dendrogram, which can be visually explored. Wish to learn more about the topic discussed in this article? k means Clustering python, packed with extra and worthwhile details to enhance your study.
Both K-Means clustering and Hierarchical clustering are essential clustering techniques that are widely used in various fields. Choosing between the two depends on the application and the specific dataset. K-Means clustering is suitable for large datasets and where the number of clusters is predetermined. Hierarchical clustering, on the other hand, is more computationally intensive, but it can produce more understandable insights into the dataset structure without prior knowledge of the number of clusters.
Dive deeper into the subject by visiting the related posts. Explore and learn:
In the event you loved this post and you would like to receive much more information regarding k means Clustering python kindly visit the web-page.