Why clustering techniques are relevant in the world of data science?

Clustering in machine learning

Why clustering is important?

  1. Clustering group observations so that the similar observations belonging in the same group, whereas observations in different groups are dissimilar.
  2. Clustering results can be used as a preprocessing step for other algorithms.
  3. Visualization of clusters may reveal some important information of data.
  4. Clustering can be considered as a stand alone tool to get insight into data distribution.

Different methods of clustering

There are two primary approaches to clustering; namely hierarchical or agglomerative clustering and k-means clustering.

Classification of clustering.
Steps involved in hierarchical clustering
Flowchart that depicts the steps involved in K-means clustering
  1. Image processing — the images can be clustered based on their visual content.
  2. Web- the different web pages can be clustered based on their content. The web users can be clustered based on their webpage access patterns.
  3. Finance- cluster analysis can be used for creating balanced portfolios.
  4. Market segmentation-customers can be grouped into clusters based on demographic information and transaction history , and a marketing strategy is tailored for each segment.

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