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Network sparsification system for mining forward small groups

A sparse and group technology, applied in the field of enterprise management, can solve problems such as unfair and unfair voting scoring, inaccurate information collection, and difficulty in finding positive small groups, and achieve the effect of clear design ideas and complete design structure

Pending Publication Date: 2020-05-19
北京时代天鉴科技发展有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is that in the mutual evaluation of employees of the enterprise, various types of small groups conduct unfair and unjust voting scores according to their own group interests, resulting in inaccurate information collection and positive small groups representing correct interests Difficult to be found, not only makes it difficult to use excellent human resources, but also makes it difficult for enterprises to develop healthily

Method used

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  • Network sparsification system for mining forward small groups
  • Network sparsification system for mining forward small groups
  • Network sparsification system for mining forward small groups

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0065] This application argues that, without the influence of small groups, each individual's score should be very close to his average (note that the standard score has removed the influence of scoring habits). Then, the difference between the score (standard score) given by an employee i to employee j and the average score of employee j's standard score reflects the "prejudice" of employee i against employee j. In this patent, we believe that this prejudice is arising from their small group affiliation.

[0066] Employee i's bias towards employee j (rating bias)

[0067] = Employee i's score on employee j (standard score) - the average score of employee j's standard score

[0068] Below is a more mathematical expression, along with the deviation matrix.

[0069]

[0070] Depend on Figure 7 It can be seen that "excellent and independent members will get lower bonus points", "poor and independent members will get lower deduction points", and the former is easy to get a ...

Embodiment 2

[0076] First, a few commonly used notations in this section are briefly described:

[0077] ——n: each person selects no more than n people (only the n people with the highest scores are retained);

[0078] ——N: If the equal score is the highest score and the number of employees does not exceed N, then these employees with the highest score will be marked as excellent employees;

[0079] ——M: The employees whose total number of votes exceeds M are considered to be "real outstanding employees".

[0080] Figure 19 , Figure 20 and Figure 21 Data tables and visualization models are given for n=5, N=10, M=10.

[0081] In addition, the terms "first" and "second" are only used for descriptive purposes, and should not be construed as indicating or implying relative importance or implying the number of indicated technical features. Thus, the features defined with "first" and "second" may expressly or implicitly include one or more of the features, and in the description of the p...

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Abstract

The invention discloses a network sparsification system for mining a forward small group, and the system comprises a system assembly which comprises a data collection module, a deviation matrix sparsification module, and a fuzzy mathematics sparsification module. The data collection module comprises a standard sub-module and an average sub-module; the deviation matrix sparsification module comprises a formula module, a statistical attribute module, a threshold setting module and a positive deviation generation module; the statistical attribute module comprises a row attribute unit and a columnattribute unit; the fuzzy mathematics sparsification module comprises a marking module, a threshold setting module and a positive deviation generation module. Compared with the prior art, the methodhas the advantages that scoring data is subjected to sparsification processing through two independent network sparsification modules, abnormal side data in the data is determined by using different set thresholds, and a corresponding visualization model is generated by using a forward bias generation module, so that a user can intuitively discover a forward small group, the overall design idea isclear, and the design architecture is complete.

Description

technical field [0001] The invention relates to the field of enterprise management, in particular to a network sparse system for mining positive small groups. Background technique [0002] As a commonly used method in enterprise management, employee mutual evaluation has many advantages, such as: (1) colleagues know the person being evaluated better than non-colleagues, and they know more about his work performance and work performance, so the evaluation will be more accurate; (2) ) The pressure brought about by the evaluation of colleagues is a powerful factor for the assessee, which is conducive to his investment in work and the improvement of work efficiency; (3) The opinions reflected in the evaluation of colleagues are multi-faceted Yes, and it is specific to a specific job or behavior, not to an individual. [0003] However, considering the actual situation, it cannot truly reflect the phenomenon in enterprise management, and there must be many small groups driven by ...

Claims

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Application Information

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IPC IPC(8): G06F16/2458G06F16/26G06F17/16G06Q10/06
CPCG06F16/2465G06F16/26G06F17/16G06Q10/06398
Inventor 黄煜可吕继祥吕淑懿吕志成
Owner 北京时代天鉴科技发展有限公司
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