Faster Neighbor Search Using Inverted File Index

Inverted File Index (IFV) is an approximate nearest neighbor search technique that dramatically improves performance over exhaustive kNN search by partitioning data using k-means clustering. The method creates centroids for each partition and maps data points to their closest centroid. During search, it first finds the nearest centroid to the query, then searches only within that partition's data points. This reduces time complexity from O(ND) to O(KD + ND/K), achieving up to 100x speed improvements in large datasets while accepting some accuracy trade-offs.