AI is here, it is everywhere nowadays. One of the main things that runs those all these clustering algorithms. Wait what is clustering in the first place??? In this article I will try to understand and explore the deep ocean.
Clustering algorithms are a type of unsupervised machine learning algorithm that are used to find groups of similar data points in a dataset. Clustering algorithms do not require any labeled data, which means that they can be used to find patterns in data that would not be visible if the data were labeled.
This involves dividing customers into groups based on their purchase history, demographics, and other factors. This can help businesses to target their marketing campaigns more effectively.
This involves finding patterns in user behavior on websites. This can help businesses to improve the design of their websites and to target their advertising more effectively.
This involves analyzing biological data, such as gene expression data. This can help researchers to identify genes that are involved in diseases and to develop new treatments.
This involves finding objects and other features in images. This can be used for tasks such as face recognition, object detection, and medical image analysis.
This can be used to explore unlabeled data and identify hidden patterns.
This can be used to reduce the dimensionality of data by representing each data point as the centroid of its cluster.
This can be used to identify outliers, which are data points that are significantly different from the rest of the data.
This can be used to recommend products or services to users based on their past behavior.
- k-Means Clustering
- Density-based Spatial Clustering of Applications with Noise (DBSCAN)
- Gaussian Mixture Model (GMM)
- Agglomerative Hierarchical Clustering (AHC)
- Spectral Clustering
This will be a multipart series which will follow up with more clustering algorithms with their working, pseudocode, advantages and disadvantages.
Please stay tuned for more such content.
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