Advantages Over Known Clustering Paradigms Clustering methods in use today have three major drawbacks. Most are very slow, they will not work for large data spaces, and they fail when useless dimensions are included. The source of these drawbacks is the distance metric used to assign points to clusters.
Distances become useless to discriminate clusters.
Another problem concerns scaling each dimension before distances are calculated. FFC avoids these drawbacks by using no distance metric. If each dimension has a dedicated processor, then increasing M doesn't increase the run time. FFC's execution time is proportional to NlogN, which is independent of M and very fast. In addition, FFC is visual and intuitive, unlike neural networks used for clustering.