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Hierarchical clustering and k-means based clustering are two common methods that are used in data analysis as well as machine learning to cluster related data points. Both methods aim to identify clusters in a data set but they differ in the way they approach and the type of clusters they create. This article we'll examine the differences between hierarchical clustering and K-means clustering in depth.
Data Science Course in Pune
Hierarchical Clustering Hierarchical clustering can be described as an approach from the bottom up that is also referred to as agglomerative clumping. It begins by treating each data point as separate cluster. It then joins the most close clusters in a series of iterative steps until a single cluster is left. This process creates a hierarchical structure for clusters, which is often depicted as dendrograms.
Two primary kinds of hierarchical clustering:
Agglomerative clustering This starts by treating every data point being an individual cluster, and then gradually merges the clusters closest to it until there is only one cluster left. The merging is dependent on the measure of dissimilarity or similarity between clusters, including Euclidean distance, or correlation coefficients.
Dividesive Clustering The process begins with the entire set of the data points of the same cluster and splits them up into smaller clusters until every data point is located in their own group. This approach is more uncommon and more expensive computationally in comparison to agglomerative aggregation.
Hierarchical clustering doesn't need a predetermined number of clusters as it establishes a cluster hierarchy which allows for various levels of detail. It provides an illustration of the clustering process using the dendrogram. This could be helpful in exploratory analysis and finding the ideal quantity of clusters.
K-means Clustering: K means clustering is an iterative method of partitioning an entire dataset into a set quantity (k) of exclusive mutually bonded clusters. It's aim is to minimize the amount of distances that are squared between the points of data and their respective cluster centersoids.
Data Science Course in Pune
Hierarchical Clustering Hierarchical clustering can be described as an approach from the bottom up that is also referred to as agglomerative clumping. It begins by treating each data point as separate cluster. It then joins the most close clusters in a series of iterative steps until a single cluster is left. This process creates a hierarchical structure for clusters, which is often depicted as dendrograms.
Two primary kinds of hierarchical clustering:
Agglomerative clustering This starts by treating every data point being an individual cluster, and then gradually merges the clusters closest to it until there is only one cluster left. The merging is dependent on the measure of dissimilarity or similarity between clusters, including Euclidean distance, or correlation coefficients.
Dividesive Clustering The process begins with the entire set of the data points of the same cluster and splits them up into smaller clusters until every data point is located in their own group. This approach is more uncommon and more expensive computationally in comparison to agglomerative aggregation.
Hierarchical clustering doesn't need a predetermined number of clusters as it establishes a cluster hierarchy which allows for various levels of detail. It provides an illustration of the clustering process using the dendrogram. This could be helpful in exploratory analysis and finding the ideal quantity of clusters.
K-means Clustering: K means clustering is an iterative method of partitioning an entire dataset into a set quantity (k) of exclusive mutually bonded clusters. It's aim is to minimize the amount of distances that are squared between the points of data and their respective cluster centersoids.