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Supplier close relation identification method based on community discovery and association rule analysis

A technology of community discovery and relationship identification, applied in relational databases, commerce, data processing applications, etc., can solve the problem of inability to select the list of communities suspected of collusion and collusion, difficult to meet the needs of behavior identification and analysis of collusion and collusion, and unfavorable models Problems such as parameter setting, to achieve the effect of reducing audit risk, shortening investigation time, and covering a wide range

Pending Publication Date: 2022-01-11
浙江浙能数字科技有限公司 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In practical applications, the above methods are still difficult to meet the needs of the identification and analysis of collusion behavior
On the one hand, simple association rule analysis cannot select a list of communities suspected of colluding with high accuracy, and there will be a lot of redundant information in the output results
On the other hand, the quality of the data itself has a certain adverse effect on the application of the frequent set item analysis model. Different types of bids are mixed in the same database without classification, which is not conducive to the setting of model parameters, resulting in unsatisfactory output results.

Method used

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  • Supplier close relation identification method based on community discovery and association rule analysis
  • Supplier close relation identification method based on community discovery and association rule analysis
  • Supplier close relation identification method based on community discovery and association rule analysis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0060] Embodiment 1 of the present application provides a supplier close relationship identification method based on community discovery and association rule analysis:

[0061] Step 1. Eliminate invalid data and extract key data;

[0062] Step 2. Based on the key data, traverse all sourcing single entries of the key data, form a network covering all supplier relationships, and generate an undirected graph; each node in the undirected graph represents a supplier, and according to the status of each supplier The number of occurrences in the same segment is used to calculate the weight value of the edge between each node;

[0063] Step 3. According to the undirected graph generated in step 2, the nodes of each undirected graph are used as independent communities to calculate the modularity:

[0064]

[0065] In the above formula, A ij Represents the edge weight between node i and node j; k i Represents the sum of the weights of all edges connected to node i, k j Represents...

Embodiment 2

[0078] On the basis of Example 1, as figure 1 As shown, the second embodiment of the present application uses the bidding history data obtained from the source management of an enterprise's ERP system to verify the effectiveness of the method proposed in the first embodiment: the data content includes the bid section number, bidding method, and bid opening time , bidder code, bidder name, quotation amount, bid winner, bidder name, bid amount. The characteristics of the data include: 1) There are various types of projects, including the procurement of general equipment, special equipment and other materials, as well as the procurement of engineering services, technology development and other services; 2) The number of bidders for different bids may be different; 3) There may be multiple winning bids for a single project people.

[0079] Step 1. According to actual business needs, eliminate invalid data (including repeated suppliers in the same sourcing form, missing original s...

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Abstract

The invention relates to a supplier close relation identification method based on community discovery and association rule analysis, and the method comprises the steps: removing invalid data and extracting key data according to actual business demands; based on the key data, traversing all source searching single entries of the key data to form a network covering all supplier relationships, and generating an undirected graph; and calculating modularity by taking nodes of each undirected graph as independent communities. The beneficial effects of the invention are that a reliable supplier close relation identification auditing method is established through a data mining mode, historical collection data of an enterprise is analyzed, a group community of the enterprise is identified, an internal relation structure of the enterprise is obtained, and the close relation between suppliers is found. The screening result is high in precision, the coverage range is wide, the troubleshooting time is greatly shortened, auditing personnel can mainly focus on the screened suspected bid gang list, the audit coverage can be greatly improved, the bid gang discovery rate is improved, and the audit risk is reduced.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to a supplier close relationship identification method based on community discovery and association rule analysis. Background technique [0002] Since the implementation and promotion of bidding and bidding procurement requirements, it is not uncommon for suppliers to collude in the procurement and bidding process of enterprises. For enterprise procurement, this behavior may have an adverse impact on the quality of bidding projects. Companies that win bids through unfair competition do not pay enough attention to product quality or technical requirements in the provision of material supplies or engineering services, and even shoddy, cut corners, or increase procurement costs in disguise through unreasonable changes and quotations. In addition, this behavior undermines the level playing field and may lead to corruption. [0003] In this regard, a common audit strate...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/215G06F16/2458G06F16/28G06F16/901G06Q30/00G06Q30/08
CPCG06F16/215G06F16/2468G06F16/285G06F16/9024G06Q30/018G06Q30/08G06F2216/03
Inventor 罗天李汉秋柴真琦金仲文尤震章崎峰吴林峰
Owner 浙江浙能数字科技有限公司
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