Solving Unsupervised Learning Problems

Unsupervised learning problems, when no output variable is present, seek to find some kind of structure in the underlying data, such as groups or clusters of attributes, according to their similarity: two examples belonging to the same group must exhibit a higher value of similarity than two patterns associated with different clusters.

Techniques related to unsupervised learning are usually called clustering algorithms. The number k of clusters may be chosen initially by the user (e.g. in the k-means technique) or suggested by the algorithm.

Available unsupervised learning tasks

• Solving Association Problems in Rulex
  • Using Anomaly Detection to Solve Association Problems
  • Using Hierarchical Basket Analysis to Solve Association Problems
  • Using Frequent Itemsets Mining to Solve Association Problems
  • Using Sequence Analysis to Solve Association Problems
  • Using Assortment Optimizer to Solve Association Problems
  • Using Similar Items Detector to Solve Association Problems
• Solving Clustering Problems
  • Using Label Clustering to Cluster Data
  • Using Projection Clustering to Cluster Data
  • Using Standard Clustering to Cluster Data