Iterative Clustering De-weighting Hidden Data Patterns

The patent segments the clustering process into multiple passes, where each pass focuses on a specific similarity measure or data aspect. By dividing the complex clustering task into sequential stages (first pass, second pass, etc.), each handling a particular perspective, the system manages complexity while maintaining multiple clustering viewpoints. Each pass produces clusters based on specific criteria, making the overall structure more interpretable.

The patent introduces a temporal dimension to clustering by performing multiple passes over the data. Instead of attempting to visualize all clustering perspectives simultaneously in one static structure, the system unfolds them across multiple passes/time steps. This transforms the complexity from spatial (multiple dimensions at once) to temporal (sequential exploration), making the data structure more manageable and interpretable.

Each clustering pass performs preliminary action by identifying and clustering data points based on current similarity measures before the next pass begins. The first pass establishes initial clusters that serve as a foundation for subsequent passes. This preliminary clustering reduces the search space for hidden clusters in later passes, enabling discovery of tangential clusters more efficiently than if all passes were performed without leveraging previous results.

The patent employs periodic action by repeating the clustering process in discrete passes, where each pass applies a specific similarity measure or data transformation. This periodic application of clustering with varying parameters allows the system to systematically explore different data aspects and reveal hidden clusters at appropriate intervals, balancing thoroughness with computational efficiency.