What is an example of a batch learning algorithm used for clustering tasks?Select one:a.DBSCANb.Agglomerative clusteringc.All of the aboved.K-means

Break Down the Problem

1. Identify which of the given options are considered batch learning algorithms used specifically for clustering tasks.
2. Analyze each option to determine if it fits the criteria.

Relevant Concepts

• Batch Learning Algorithm: A learning algorithm that processes the entire dataset at once to perform learning, rather than updating incrementally with each new observation.
• Clustering Algorithms: Techniques used to group data points based on similar characteristics.

Analysis and Detail

1. DBSCAN:

   • Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is typically an online clustering algorithm as it does not require a preset number of clusters and works with points incrementally.

2. Agglomerative Clustering:

   • This is a hierarchical clustering method that starts with each data point as a single cluster and merges them. This is considered batch learning since it analyzes the entire dataset to form the hierarchy.

3. K-means:

   • K-means is a batch learning algorithm since it requires the whole dataset during the initial computation to determine the centroids and assign data points to clusters.

4. All of the above:

   • This option suggests that all listed methods are batch learning algorithms used for clustering.

Verify and Summarize

• DBSCAN is not a batch learning algorithm in the traditional sense.
• Agglomerative clustering and K-means are both batch learning algorithms.
• Therefore, the correct choice is not "All of the above".

Final Answer

The correct answer is b. Agglomerative clustering and K-means (noting that DBSCAN does not fit the criteria).