Features

The key features of Advanced Clustering Cloud Service include:

• Scenario-based cluster generation, based on store or product attributes, customer segment profiles, or performance.

• Three-step cluster-generation process.

• What-if capabilities that can be used to create multiple clustering scenarios and then measure them against one another. This can help ensure that the most appropriate clusters are used by the applicable planning and execution processes.

• Automatic ranking of cluster scenarios to support what-if comparisons. Recommendations for the optimal cluster scenario and number of clusters are provided.

• Dynamic nesting of clusters, in which nested or mixed attribute clusters are created based on multiple attributes, performance data, and customer segments.

• Two types of algorithms are used.

  • Proprietary BaNG (Batch Neural Gas) algorithm for convergent cluster parameters

  • K-means approach for creating clusters in a hierarchical manner, which automatically determines the best attributes to split into an additional cluster.

• A variety of distance metrics that are suitable for real-value attributes, categorical attributes, profile-based measurements, and time-based performance.