Nearest neighbor clustering

What Nearest neighbor clustering is

Nearest neighbor clustering is a type of unsupervised machine learning algorithm used to group unlabeled data points. The algorithm works by first finding the closest data points to each other and then grouping them together into clusters. It is based on the idea that similar data points should be grouped together and dissimilar points should be kept separate.

Steps for Nearest Neighbor Clustering:

  1. Calculate the distance between each pair of data points.

  2. Select a data point as the starting point for a cluster and label it as the “center” of the cluster.

  3. Find the data point that is closest to the center and add it to the cluster.

  4. Find the data point that is closest to the new cluster and add it to the cluster.

  5. Repeat step 4 until no more data points can be added to the cluster.

  6. Repeat steps 2-5 for each data point until all data points have been assigned to a cluster.

Examples

  1. Nearest neighbor clustering is often used to identify clusters of similar objects in a dataset. For example, it can be used to group customers with similar purchasing habits or to group similar images.

  2. Nearest neighbor clustering can also be used to identify outliers in a dataset. For example, it can be used to identify customers with spending habits that are dissimilar to other customers in the dataset.

  3. Nearest neighbor clustering can also be used to detect the presence of clusters of data points that are not necessarily related to one another. For example, it can be used to identify clusters of cities with similar weather patterns.

Categories

Types of Nearest Neighbor Clustering:

  1. K-Nearest Neighbor Clustering (KNN)
  2. Locally Sensitive Hashing (LSH)
  3. Hierarchical Agglomerative Clustering (HAC)
  4. Mean-shift Clustering
  5. Density-based Spatial Clustering of Applications with Noise (DBSCAN)
  6. OPTICS Clustering

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