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Questions & Answers › Tag: Clustering
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What is the R function to apply hierarchical clustering to a matrix of distance objects?
AnsweredAdam asked 3 years ago • 
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What is the R Function to divide a dataset into k clusters?
AnsweredAdam asked 3 years ago • 
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Members of the same cluster are far away / distant from each other.
AnsweredAdam asked 3 years ago • 
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_____________ is when points don't move between clusters and centroids stabilize.
AnsweredAdam asked 3 years ago • 
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K Means algorithm assumes Euclidean Space/Distance.
AnsweredAdam asked 3 years ago • 
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___________ is a way of finding the k value for k means clustering.
AnsweredAdam asked 3 years ago • 
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Hierarchical Clustering is a suggested approach for Large Data Sets.
AnsweredAdam asked 3 years ago • 
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Sampling is one technique to pick the initial k points in K Means Clustering.
AnsweredAdam asked 3 years ago • 
6266 views1 answers-1 votes
The number of rounds for convergence in k means clustering can be large.
AnsweredAdam asked 3 years ago • 
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A centroid is a valid point in a non-Euclidean space.
AnsweredAdam asked 3 years ago • 
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The ______ is a visual representation of how the data points are merged to form clusters.
AnsweredAdam asked 3 years ago • 
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___________ measures the goodness of a cluster.
AnsweredAdam asked 3 years ago • 
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___________ is the data point that is closest to the other point in the cluster.
AnsweredAdam asked 3 years ago • 
4158 views1 answers1 votes
What is the overall complexity of the the Agglomerative Hierarchical Clustering?
AnsweredAdam asked 3 years ago • 
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_____________ of a set of points is defined using a distance measure.
AnsweredAdam asked 3 years ago • 
7069 views1 answers1 votes
Which learning is the method of finding structure in the data without labels.
AnsweredAdam asked 3 years ago • 
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Unsupervised learning focuses on understanding the data and its underlying pattern.
AnsweredAdam asked 3 years ago • 
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What is a preferred distance measure while dealing with sets?
AnsweredAdam asked 3 years ago • 
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Each point is a cluster in itself. We then combine the two nearest clusters into one. What type of clustering does this represent ?
AnsweredAdam asked 3 years ago • 
5815 views1 answers0 votes
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