Early Warning Management of Enterprise Human Resource Crisis Based on Fuzzy Data Mining Algorithm of Computer

2020 ◽  
pp. 640-645
Author(s):  
Hua Wu
Keyword(s):  
Data Mining ◽  
Human Resource ◽  
Early Warning ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Mining Algorithm ◽  
Early Warning Management ◽  
Human Resource Crisis

scholarly journals Human Resource Allocation Based on Fuzzy Data Mining Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
You Wu ◽  
Zheng Wang ◽  
Shengqi Wang
Keyword(s):  
Data Mining ◽  
Human Resource ◽  
Business Process ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Data Mining Algorithm ◽  
Rule Mining ◽  
Mining Algorithm ◽  
Database Technology

Data mining is currently a frontier research topic in the field of information and database technology. It is recognized as one of the most promising key technologies. Data mining involves multiple technologies, such as mathematical statistics, fuzzy theory, neural networks, and artificial intelligence, with relatively high technical content. The realization is also difficult. In this article, we have studied the basic concepts, processes, and algorithms of association rule mining technology. Aiming at large-scale database applications, in order to improve the efficiency of data mining, we proposed an incremental association rule mining algorithm based on clustering, that is, using fast clustering. First, the feasibility of realizing performance appraisal data mining is studied; then, the business process needed to realize the information system is analyzed, the business process-related links and the corresponding data input interface are designed, and then the data process to realize the data processing is designed, including data foundation and database model. Aiming at the high efficiency of large-scale database mining, database development tools are used to implement the specific system settings and program design of this algorithm. Incorporated into the human resource management system of colleges and universities, they carried out successful association broadcasting, realized visualization, and finally discovered valuable information.

A Fuzzy Data Mining Algorithm for Finding Sequential Patterns

2003 ◽  
Vol 11 (02) ◽  
pp. 173-193 ◽  
Author(s):  
Yi-Chung Hu ◽  
Ruey-Shun Chen ◽  
Gwo-Hshiung Tzeng ◽  
Jia-Hourng Shieh
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Sequential Patterns ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Effective Algorithm ◽  
Mining Algorithm ◽  
Fuzzy Knowledge

Since fuzzy knowledge representation can facilitate interaction between an expert system and its users, the effective construction of a fuzzy knowledge base is important. Fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, and can thus be helpful in building a prototype fuzzy knowledge base. We define that a fuzzy sequence is an ordered list of frequent fuzzy grids, and the length of a fuzzy sequence is the number of frequent fuzzy grids in the frequent fuzzy sequence. Frequent fuzzy grids and frequent fuzzy sequences can be determined by comparing individual fuzzy supports with the user-specified minimum fuzzy support. A fuzzy sequential pattern is just a frequent fuzzy sequence, but it is not contained in any other frequent fuzzy sequence. In this paper, an effective algorithm called the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) is proposed to generate fuzzy sequential patterns. A numerical example is used to show an analysis of the user visit to websites, demonstrating the usefulness of the proposed algorithm.

A FUZZY DATA MINING ALGORITHM FOR INCREMENTAL MINING OF QUANTITATIVE SEQUENTIAL PATTERNS

2005 ◽  
Vol 13 (06) ◽  
pp. 633-652 ◽  
Author(s):  
R. B. V. SUBRAMANYAM ◽  
A. GOSWAMI
Keyword(s):  
Data Mining ◽  
Real World ◽  
Uncertain Data ◽  
Sequential Patterns ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Incremental Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithm ◽  
Real World Applications

In real world applications, the databases are constantly added with a large number of transactions and hence maintaining latest sequential patterns valid on the updated database is crucial. Existing data mining algorithms can incrementally mine the sequential patterns from databases with binary values. Temporal transactions with quantitative values are commonly seen in real world applications. In addition, several methods have been proposed for representing uncertain data in a database. In this paper, a fuzzy data mining algorithm for incremental mining of sequential patterns from quantitative databases is proposed. Proposed algorithm called IQSP algorithm uses the fuzzy grid notion to generate fuzzy sequential patterns validated on the updated database containing the transactions in the original database and in the incremental database. It uses the information about sequential patterns that are already mined from original database and avoids start-from-scratch process. Also, it minimizes the number of candidates to check as well as number of scans to original database by identifying the potential sequences in incremental database.

Mining Frequent Fuzzy Grids in Dynamic Databases with Weighted Transactions and Weighted Items

2006 ◽  
Vol 05 (03) ◽  
pp. 243-257
Author(s):  
R. B. V. Subramanyam ◽  
A. Goswami
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Initial Level ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Incremental Mining ◽  
Mining Algorithm ◽  
The Status ◽  
Dynamic Databases ◽  
Mining Algorithms

Incremental mining algorithms that derive the latest mining output by making use of previous mining results are attractive to business organisations. In this paper, a fuzzy data mining algorithm for incremental mining of frequent fuzzy grids from quantitative dynamic databases is proposed. It extends the traditional association rule problem by allowing a weight to be associated with each item in a transaction and with each transaction in a database to reflect the interest/intensity of items and transactions. It uses the information about fuzzy grids that are already mined from original database and avoids start-from-scratch process. In addition, we deal with "weights-of-significance" which are automatically regulated as the incremental databases are evolved and implant themselves in the original database. We maintain "hopeful fuzzy grids" and "frequent fuzzy grids" and our algorithm changes the status of the grids which have been discovered earlier so that they reflect the pattern drift in the updated quantitative databases. Our heuristic approach avoids maintaining many "hopeful fuzzy grids" at the initial level. The algorithm is illustrated with one numerical example and demonstration of experimental results are also incorporated.

scholarly journals Understanding Open Source Software Evolution Using Fuzzy Data Mining Algorithm for Time Series Data

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Munish Saini ◽  
Sandeep Mehmi ◽  
Kuljit Kaur Chahal
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Open Source ◽  
Open Source Software ◽  
Time Series Data ◽  
Series Data ◽  
Software Project ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Mining Algorithm

Source code management systems (such as Concurrent Versions System (CVS), Subversion, and git) record changes to code repositories of open source software projects. This study explores a fuzzy data mining algorithm for time series data to generate the association rules for evaluating the existing trend and regularity in the evolution of open source software project. The idea to choose fuzzy data mining algorithm for time series data is due to the stochastic nature of the open source software development process. Commit activity of an open source project indicates the activeness of its development community. An active development community is a strong contributor to the success of an open source project. Therefore commit activity analysis along with the trend and regularity analysis for commit activity of open source software project acts as an important indicator to the project managers and analyst regarding the evolutionary prospects of the project in the future.

scholarly journals Construction of Social Security Fund Cloud Audit Platform Based on Fuzzy Data Mining Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yangting Huai ◽  
Qianxiao Zhang
Keyword(s):  
Data Mining ◽  
Social Security ◽  
Data Mining Algorithm ◽  
Fuzzy Data ◽  
Social Security Fund ◽  
Mining Algorithm ◽  
The Neural Network ◽  
Security Fund ◽  
Security Funds ◽  
Cloud Audit

Guided by the theories of system theory, synergetic theory, and other disciplines and based on fuzzy data mining algorithm, this article constructs a three-tier social security fund cloud audit platform. Firstly, the article systematically expounds the current situation of social security fund and social security fund audit, such as the technical basis of cloud computing and data mining. Combined with the actual work, the necessity and feasibility of building a cloud audit platform for social security funds are analyzed. This article focuses on the construction of the cloud audit platform for social security funds. The general idea of using fuzzy data mining algorithm to build the social security fund audit cloud platform is to compress the knowledge contained in a large number of data into the weights between nodes and optimize the weights through the learning of the neural network system. Through the optimization function, the information contained in the neural network is stored in a few weights as far as possible. The main information is further highlighted by network clipping and removing weights that have little impact on the output.

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