a tree algorithm for nearest neighbor searching in document retrieval systems
The problem of finding nearest neighbors to a query in a document collection is a special case of associative retrieval, in which searches are performed using more than one key. A nearest neighbors associative retrieval algorithm, suitable for document retrieval using similarity matching, is described. The basic structure used is a binary tree, at each node a set of keys (concepts) is tested to select the most promising branch. Backtracking to initially rejected branches is allowed and often necessary. Under certain conditions, the search time required by this algorithm is 0(log 2 N) k . N is the number of documents, and k is a system-dependent parameter. A series of experiments with a small collection confirm the predictions made using the analytic model; k is approximately 4 in this situation. This algorithm is compared with two other searching algorithms; sequential search and clustered search. For large collections, the average search time for this algorithm is less than that for a sequential search and greater than that for a clustered search. However, the clustered search, unlike the sequential search and this algorithm, does not guarantee that the near neighbors found are actually the nearest neighbors.
tree algorithm nearest neighbor search document retrieval documentation retrieval systems query collect case association similarity matching structure concept select backtracking timing experience predict situated cluster dependencies nearest-neighbor search