The divisive clustering algorithm is a top-down clustering approach in which all points in the dataset are initially assigned to one cluster and then split iteratively as one progresses down the hierarchy.
Agglomerative The hierarchical clustering method allows clusters to be read from bottom to top, and the algorithm always reads from the sub-component first before moving to the parent. Divisive, on the other hand, employs a top-down method in which the parent is visited first, followed by the child.
Agglomerative clustering is done from the bottom up, with each data point starting in its own cluster. These clusters are then greedily joined by merging the two clusters that are the most similar. Divisive clustering works from the top down, with all data points starting in the same cluster.
A divide-and-conquer algorithm is also more precise. Without first examining the global distribution of data, agglomerative clustering makes judgments based on local patterns or neighbour points. These early decisions are irreversible.
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Rishu Shrivastav
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explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
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VIKAS CHOUBEY
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very nicely explained
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Vrushali Kandesar
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Awesome and very nicely explained!!!
One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)
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Mohd Mushraf
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Amazing Teaching
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Juboraj Juboraj
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Easy to understand & explain details.
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Joydeb
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Awesome Course sir and your teaching style is very GOOD.
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Shaga Chandrakanth Goud
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Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld
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Neel Khairnar
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Kushal is very good explainer he is covering all topics nicely 👍
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