Unsupervised Learning: Clustering and Collaborative Filtering
Clustering
Clustering is a type of unsupervised learning where the goal is to group
similar data points together based on certain features or attributes. There
are several algorithms used for clustering, including:
K-means clustering
Hierarchical clustering
Density-based spatial clustering of applications with noise
(DBSCAN)
K-means Clustering
K-means clustering is a simple and widely used clustering algorithm. It
aims to partition the data into K distinct, non-overlapping clusters where
each data point belongs to the cluster with the nearest mean. The steps
involved in K-means clustering are:
. Initialize K centroids randomly.
. Assign each data point to the nearest centroid.
. Calculate new centroids based on the mean of the assigned data
points.
. Repeat steps 2-3 until convergence (i.e., when the centroids no
longer change significantly).
Clustering
Clustering is a type of unsupervised learning where the goal is to group
similar data points together based on certain features or attributes. There
are several algorithms used for clustering, including:
K-means clustering
Hierarchical clustering
Density-based spatial clustering of applications with noise
(DBSCAN)
K-means Clustering
K-means clustering is a simple and widely used clustering algorithm. It
aims to partition the data into K distinct, non-overlapping clusters where
each data point belongs to the cluster with the nearest mean. The steps
involved in K-means clustering are:
. Initialize K centroids randomly.
. Assign each data point to the nearest centroid.
. Calculate new centroids based on the mean of the assigned data
points.
. Repeat steps 2-3 until convergence (i.e., when the centroids no
longer change significantly).
