This component can be used for solving one of the most popular unsupervised machine learning problems - Data Segmentation or Cluster Analysis. Unsupervised learning unites the algorithms that learn patterns from untagged data.
The primary goal of unsupervised machine learning is to discover some previously unknown patterns in data and use them for :
Unlike Supervised learning that requires the labeled data for the model training and validation, in the case of Unsupervised learning, we do not have any ground trues or the golden standard, which could be used for the models' quality grounding, but the understanding of the business domain and the nature of data allows to choose the most appropriate approach for the patterns' extraction and the decision making.
According to the definition, Segmentation is the dividing of something into parts, which are loosely connected. Data Segmentation has many business applications - from medicine to retail that is explained by the ability to apply data mining techniques for patterns discovery and cluster analysis.
Cluster analysis task can be solved via various algorithms that depend on:
The result of cluster analysis strongly depends on data and is regulized via algorithms' hyperparameters.
Undoubtedly, cluster analysis as part of the Data Mining discipline requires an experienced data scientist who can combine domain knowledge and data mining experience to obtain the results that will have business value. Still, for most problems that relate to the marketing domain, the Data Segmentation pipeline can be represented as the following process:
The Data Preprocessing stage depends on not only data but the Clustering algorithm as well. One of the popular clustering algorithms is K-Means Clustering, so it may be considered a universal solution. ****This algorithm belongs to the centroid-based clustering group and allows iteratively find the optimal clusters' centroids when the number of clusters is defined. K-means algorithm has the following advantages: