4 results about "Cluster graph" patented technology

The present disclosure relates to an approximate nearest neighbor search method and an approximate nearest neighbor search system. According to the embodiment, the approximate nearest neighbor search method manages graph index information for defining an inter-cluster graph. The approximate nearest neighbor search method searches for a vector closest to a query from vectors belonging to a search start cluster having a reference position closest to the query among a plurality of clusters. The approximate nearest neighbor search method selects one or more search target clusters close to the search start cluster while advancing along the inter-cluster graph, and searches for a vector closest to the query from vectors belonging to each of the one or more search target clusters. Thus, the approximate nearest neighbor search method capable of reducing the amount of data required to be rewritten accompanying update of the graph index and capable of obtaining sufficient search accuracy is provided.

According to one embodiment, an approximate nearest neighbor search method manages graph-based index information for defining an inter-cluster graph. The approximate nearest neighbor search method searches for a vector closest to a query vector from vectors belonging to a search start cluster that is closest to the query vector among a plurality of clusters. The approximate nearest neighbor search method selects one or more search target clusters close to the search start cluster while traversing the inter-cluster graph, and searches for a vector closest to the query vector from vectors belonging to each of the one or more search target clusters.

The application discloses a news event prediction method based on heterogeneous evolution event clustering, comprising the following steps: generating event representation based on the preliminary updated entity representation and relationship representation in the constructed entity graph, regarding the event as a node, regarding the heterogeneous relationship between events as an edge, and constructing an event graph; obtaining event clusters by fuzzy clustering and constructing an event cluster graph; optimizing the event cluster representation according to the distance and similarity between event clusters on the event cluster graph by using a self-supervised optimization algorithm; capturing the implicit correlation between event clusters by using an implicit relationship encoder, and then updating the representation of the event cluster, the representation of the event, the entity and the relationship representation in sequence after sparsification and information aggregation; and predicting by a convolution-based news event model. The application effectively models the pair correlation, high-order correlation and multi-step time sequence evolution between events, and has important application value in international situation analysis, social governance and intelligent decision support.