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Non-Hierarchical Clustering

This presentation educates you about Non-Hierarchical Clustering, Difference Hierarchical and Non-Hierarchical Clustering, K-means clustering, K-means clustering algorithm and Steps for Applying K-Means Clustering.<br><br>For more information stay tuned with Learnbay.

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Non-Hierarchical Clustering

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  1. Non-Hierarchical Clustering Swipe

  2. Non-Hierarchical Clustering Non Hierarchical Clustering involves formation of new clusters by merging or splitting the clusters. It does not follow a tree like structure like hierarchical clustering. This technique groups the data in order to maximize or minimize some evaluation criteria.K means clustering is an effective way of non hierarchical clustering. In this method the partitions are made such that non-overlapping groups having no hierarchical relationships between themselves.

  3. Difference Difference between Hierarchical Clustering and Non Hierarchical Clustering: Hierarchical Clustering involves creating clusters in a predefined order from top to bottom Non Hierarchical Clustering involves formation of new clusters by merging or splitting the clusters instead of following a hierarchical order.

  4. It is considered less reliable than Non Hierarchical Clustering. It is comparatively more reliable than Hierarchical Clustering. It is considered slower than Non Hierarchical Clustering. It is comparatavely more faster than Hierarchical Clustering. It is very problematic to apply this technique when we have data with high level of error. It can work better then Hierarchical clustering even when error is there. It is comparatively easier to read and understand. It is relatively unstable than Non Hierarchical clustering. The clusters are difficult to read and understand as compared to Hierarchical clustering. It is a relatively stable technique.

  5. K-means clustering K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K.

  6. The algorithm works iteratively to assign each data point to one of K groups based on the features that are provided. Data points are clustered based on feature similarity.

  7. K-means clustering algorithm The centroids of the K clusters, which can be used to label new data Labels for the training data (each data point is assigned to a single cluster) Rather than defining groups before looking at the data, clustering allows you to find and analyze the groups that have formed organically.

  8. The "Choosing K" section below describes how the number of groups can be determined. Each centroid of a cluster is a collection of feature values which define the resulting groups. Examining the centroid feature weights can be used to qualitatively interpret what kind of group each cluster represents.

  9. Steps for Applying K-Means Clustering Step 1: Clean and Transform Your Data Step 2: Choose K and Run the Algorithm Step 3: Review the Results Step 4: Iterate Over Several Values of K

  10. Topics for next Post T-test Chi-square Test Stay Tuned with

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