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K-Means Clustering

What is K-Means Clustering?

K-means is an unsupervised learning method that divides data points into a predefined number k of groups. It assigns each point to the nearest group centre and keeps moving these centres until the assignments barely change. The method is fast and easy to understand, but requires a sensible choice of k and features on comparable scales. Example: grouping customers by purchase frequency and basket value.

How KI.ordo helps

KI.ordo's template for grouping enquiries by topic pursues a similar goal but forms the groups with a language model rather than k-means.

Learn more →: K-Means Clustering

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Topics

  • Clustering
  • Unsupervised learning
  • Algorithm

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Related terms

Clustering

Clustering is an unsupervised learning method that divides data points into groups based on their similarity, without predefined categories. It reveals patterns nobody has named before – for example customer segments with similar buying behaviour. With texts, clustering helps sort hundreds of customer enquiries into topics such as delivery, invoicing or complaints and shows where the focus lies.

  • unsupervised learning
  • grouping
  • +1
Template

Unsupervised Learning

In unsupervised learning, a model receives data without predefined correct answers and searches for structures on its own. Typical methods are clustering, which groups similar records, and dimensionality reduction, which condenses many features into a few essential ones. Organisations use it to discover customer segments or spot unusual transactions; the groups found must then be interpreted by domain experts.

  • Machine learning
  • Clustering
  • +1

K-Nearest Neighbours

K-nearest neighbours (k-NN) is a simple supervised learning method. To classify a new data point, it looks for the k most similar known examples and adopts their most common class or, for numerical values, their average. It needs no real training phase, but becomes slow with large data sets and is sensitive to how features are scaled. Example: product recommendations based on similar customers.

  • Classification
  • Supervised learning
  • +1

Dimensionality reduction

Dimensionality reduction lowers the number of features in a data set without losing the essential information. Well-known methods include principal component analysis (PCA) as well as t-SNE and UMAP, which are often used for visualisation. It speeds up computation, reduces the risk of overfitting and makes data easier to grasp – for example when hundreds of customer attributes are mapped onto a two-dimensional chart on which similar customers sit close together.

  • data analysis
  • PCA
  • +1