A database search method for obtaining maximal complete subgraphs from m-part graphs

Abstract

The invention provides a database search method for obtaining a maximally complete subgraph from an m-part graph. The search method is applied to a face recognition database. The method forms a k-order maximally complete subgraph by establishing an undirected graph model. subgraph, and store the undirected graph G through the adjacency linked list, take the positive sequence edge and the largest vertex as the basic quantity, and use the pruning method to calculate the number of vertices of the undirected graph G and the calculation amount T of the k-order complete subgraph k Perform calculations to obtain the space complexity and time complexity of the maximally complete subgraph, complete the search for the maximally complete subgraph, and use the search results in face recognition data. By looking for frequent itemsets, the Access to the database improves recognition efficiency.

Description

technical field [0001] The invention belongs to the technical field of face research and judgment analysis systems, and in particular relates to a database search method for obtaining a maximum complete subgraph from an m-part graph. Background technique [0002] The maximal complete subgraph has a very wide range of applications. In terms of management decision-making, some management affairs problems such as personnel management and transportation scheduling can be abstracted as solving maximal complete subgraph problems. In terms of data mining, the mining of association rules is one of the important contents of data mining research. Using the maximum complete subgraph to find frequent itemsets can greatly reduce the access to the database and improve the CPU utilization. [0003] The maximum complete subgraph problem is a well-known combinatorial optimization problem, and it is also one of the earliest proven NP-complete problems. Due to the needs of theoretical resear...

AI Technical Summary

Problems solved by technology

This will cause the query result set to not be grouped by person

[0012] 3. Traditional data analysis methods mostly rely on brainstorming in the formulation of research and judgment rules for the core functional steps (such as: how to determine that multiple photos are of the same person), resulting in unstable quality of research and judgment results

[0013] 4. Traditional data analysis methods are mostly composed of some basic statistical analysis operations (accumulation, averaging, grouping, conditional filtering, etc.), while face data is just some pictures and unstructured feature data identified from these pictures , traditional data statistical analysis operations cannot be applied to unstructured data.

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