Patent ID: 6862586
Filing Date: 2005-03-01
Classification: G06F,Y10S

Abstract:
1. A method of perforating a database search comprising: searching a database using a query, said searching identifying a group of hyperlinked documents; forming a high-dimensional torus geometric representation of said hyperlinked documents, wherein each hyperlinked document is represented by a vector triplet comprising a normalized word frequency, a normalized out-link frequency and a normalized in-link frequency; clustering said result items into clusters based on said high-dimensional torus geometric representation; ranking items within each cluster of said clusters based on said high-dimensional torus geometric representation; summarizing contents of said clusters based on said high-dimensional torus geometric representation, wherein said clustering of the said vector triplets on said high-dimensional torus geometric representation is performed using a toric k-means clustering process that uses a cosine-type similarity measure between document vector triplets, thereby producing clusters of vector triplets and producing a concept triplet for each of the clusters; and summarizing said clusters of vector triplets based on nuggets of information including: identifying a closeness of said vector triplets in a cluster to said concept triplet for said cluster on said high-dimensional torus geometric representation; iidentifying said words with a highest normalized word frequency in said concept triplet for said cluster as the most frequent key-words for each of said clusters; identifying said out-links with a highest normalized out-link frequency in the concept triplet for the cluster as most frequent key out-links for each of said clusters; identifying said in-links with a highest normalized in-link frequency in the concept triplet for the cluster as most frequent important in-links for each cluster; identifying hypertext items relevant to the user's query by using a weighting of terms used in said query; identifying documents closest to said concept triplet as most typical documents in a cluster, using a cosine-type textual content similarity measure between document vector triplets; and identifying documents closest to said concept triplet as most typical documents in a cluster, using a cosine-type out-link similarity measure between document vector triplets; and identifying documents closest to said concept triplet as most typical documents in a cluster, using a cosine-type in-link similarity measure between document vector triplets.