The clustering result obtained from DBSCAN is b. Non-fuzzy complete.

Here's why:

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm, which can discover clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers.

The term "non-fuzzy" refers to the fact that DBSCAN does not use a probabilistic approach. In other words, a point belongs to a specific cluster or it doesn't, there's no in-between (which would be the case in "fuzzy" clustering).

The term "complete" refers to the fact that DBSCAN, unlike some other clustering algorithms, doesn't require the user to specify the number of clusters in the data a priori, which could lead to incomplete clustering if the specified number of clusters is less than the actual number in the data.

Therefore, the clustering result obtained from DBSCAN is non-fuzzy complete.

Question

The clustering result obtained from DBSCAN is b. Non-fuzzy complete.

Here's why:

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm, which can discover clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers.

The term "non-fuzzy" refers to the fact that DBSCAN does not use a probabilistic approach. In other words, a point belongs to a specific cluster or it doesn't, there's no in-between (which would be the case in "fuzzy" clustering).

The term "complete" refers to the fact that DBSCAN, unlike some other clustering algorithms, doesn't require the user to specify the number of clusters in the data a priori, which could lead to incomplete clustering if the specified number of clusters is less than the actual number in the data.

Therefore, the clustering result obtained from DBSCAN is non-fuzzy complete.

Knowee AI · Accepted Answer

The clustering result obtained from DBSCAN is b. Non-fuzzy complete.

Here's why:

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm, which can discover clusters of different shapes and sizes from a large amount of data, which is containing noise and outliers.

The term "non-fuzzy" refers to the fact that DBSCAN does not use a probabilistic approach. In other words, a point belongs to a specific cluster or it doesn't, there's no in-between (which would be the case in "fuzzy" clustering).

The term "complete" refers to the fact that DBSCAN, unlike some other clustering algorithms, doesn't require the user to specify the number of clusters in the data a priori, which could lead to incomplete clustering if the specified number of clusters is less than the actual number in the data.

Therefore, the clustering result obtained from DBSCAN is non-fuzzy complete.

Clustering result obtained from DBSCAN is _____ .a.Fuzzy completeb.Non-fuzzy completec.Fuzzy partiald.Non-fuzzy partial

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