Study on Data Mining Techniques and Algorithms of Association Rules Data Mining

2014 ◽  
Vol 543-547 ◽  
pp. 2040-2044
Author(s):  
Yan Bo Wang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Granular Computing ◽  
Rapid Development ◽  
System Structure ◽  
Data Set ◽  
Mining Technology ◽  
Technology System ◽  
Operation Process ◽  
Database Technology

With the rapid development of network and database technology, data need to be processed massively increased, how to carry out effective data mining is a serious problem. The mature development of granular computing algorithm provides new ideas and new methods to study for data mining. Association rules of granular computing can reduce the number of object scanning data set, and improve the efficiency of the algorithm. In this paper we introduce the data source, classification, technology, system structure, operation process, application in other areas of data mining technology. Based on association rules of granular computing, data mining technology can provide quantitative basis for enterprise in screening assessment, so the service object has a stronger competitive advantage and focus more on its problems.

Optimization of Intelligent Data Mining Technology in Big Data Environment

2019 ◽  
Vol 23 (1) ◽  
pp. 129-133 ◽  
Author(s):  
Wei Wang ◽  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Association Rules ◽  
Confidence Level ◽  
Strong Association ◽  
Processing Efficiency ◽  
Compression Technique ◽  
Data Set ◽  
Mining Technology ◽  
Data Environment

At present, storage technology cannot save data completely. Therefore, in such a big data environment, data mining technology needs to be optimized for intelligent data. Firstly, in the face of massive intelligent data, the potential relationship between data items in the database is firstly described by association rules. The data items are measured by support degree and confidence level, and the data set with minimum support is found. At the same time, strong association rules are obtained according to the given confidence level of users. Secondly, in order to effectively improve the scanning speed of data items, an optimized association data mining technology based on hash technology and optimized transaction compression technology is proposed. A hash function is used to count the item set in the set of waiting options, and the count is less than its support, then the pruning is done, and then the object compression technique is used to delete the item and the transaction which is unrelated to the item set, so as to improve the processing efficiency of the association rules. Experiments show that the optimized data mining technology can significantly improve the efficiency of obtaining valuable intelligent data.

scholarly journals An improved association rule mining algorithm for large data

2021 ◽  
Vol 30 (1) ◽  
pp. 750-762
Author(s):  
Zhenyi Zhao ◽  
Zhou Jian ◽  
Gurjot Singh Gaba ◽  
Roobaea Alroobaea ◽  
Mehedi Masud ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rapid Development ◽  
Large Data ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Mining Technology ◽  
Database Technology

Abstract The data with the advancement of information technology are increasing on daily basis. The data mining technique has been applied to various fields. The complexity and execution time are the major factors viewed in existing data mining techniques. With the rapid development of database technology, many data storage increases, and data mining technology has become more and more important and expanded to various fields in recent years. Association rule mining is the most active research technique of data mining. Data mining technology is used for potentially useful information extraction and knowledge from big data sets. The results demonstrate that the precision ratio of the presented technique is high comparable to other existing techniques with the same recall rate, i.e., the R-tree algorithm. The proposed technique by the mining effectively controls the noise data, and the precision rate is also kept very high, which indicates the highest accuracy of the technique. This article makes a systematic and detailed analysis of data mining technology by using the Apriori algorithm.

scholarly journals Precise Position Intelligent Matching System of Online Recruitment Platform Based on Data Mining Technology

2021 ◽  
Vol 2066 (1) ◽  
pp. 012001
Author(s):  
Zhen Gao
Keyword(s):  
Data Mining ◽  
Rapid Development ◽  
Internet Technology ◽  
Job Seekers ◽  
Mining Technology ◽  
Position Information ◽  
Precise Position ◽  
Random Mass ◽  
Online Recruitment ◽  
Platform System

Abstract With the rapid development of Internet technology and computer technology, network applications have been developed more and more, and have penetrated into all walks of life in society. The emergence of the networking of the talent market has made the scale of online recruitment increase, and the amount of data on the Internet has become larger and larger, and online recruitment has become the main channel for corporate recruitment. Therefore, how to use the massive online recruitment data to quickly and accurately find the corresponding information and explore the hidden knowledge mode is a very valuable research topic. Data mining (DM) is a technology for data analysis for large amounts of data. It can discover hidden, hidden, and potentially useful knowledge hidden in the data from the vague, noisy, and random mass data, and build relevant Model, realize prediction, etc. The characteristics of data mining technology (DMT) are very suitable for the analysis of online recruitment information, research on large amounts of information, and find out the knowledge in it for decision support. This article aims to study the accurate job matching system of the online recruitment platform based on DMT. Based on the analysis of the advantages of online recruitment, related DMT and the design principles of the online recruitment platform system, the data collected by Weka DM tools are analyzed. Analyzing and getting useful job positions is just to provide job seekers and corporate-related recruiters with useful job information. The experimental results show that the online recruitment platform system can complete the collection of online recruitment position information, and can realize the DM function, which has good practical application value.

Finding Persistent Strong Rules

2010 ◽  
pp. 85-107
Author(s):  
Anthony Scime ◽  
Karthik Rajasethupathy ◽  
Kulathur S. Rajasethupathy ◽  
Gregg R. Murray
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Strong Association ◽  
National Election ◽  
Data Sets ◽  
Rule Discovery ◽  
Discovery Process ◽  
Data Set ◽  
Rule Sets ◽  
Election Studies

Data mining is a collection of algorithms for finding interesting and unknown patterns or rules in data. However, different algorithms can result in different rules from the same data. The process presented here exploits these differences to find particularly robust, consistent, and noteworthy rules among much larger potential rule sets. More specifically, this research focuses on using association rules and classification mining to select the persistently strong association rules. Persistently strong association rules are association rules that are verifiable by classification mining the same data set. The process for finding persistent strong rules was executed against two data sets obtained from the American National Election Studies. Analysis of the first data set resulted in one persistent strong rule and one persistent rule, while analysis of the second data set resulted in 11 persistent strong rules and 10 persistent rules. The persistent strong rule discovery process suggests these rules are the most robust, consistent, and noteworthy among the much larger potential rule sets.

Research on Data Mining Algorithm Based on Pattern Recognition

2019 ◽  
Vol 34 (06) ◽  
pp. 2059015 ◽  
Author(s):  
Xuelong Zhang
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Rapid Development ◽  
Future Trend ◽  
Support Vector ◽  
Massive Data ◽  
Data Mining Algorithm ◽  
Rapid Improvement ◽  
Training Time ◽  
Database Technology

With the advent of the era of big data, people are eager to extract valuable knowledge from the rapidly expanding data, so that they can more effectively use these massive storage data. The traditional data processing technology can only achieve basic functions such as data query and statistics, and cannot achieve the goal of extracting the knowledge existing in the data to predict the future trend. Therefore, along with the rapid development of database technology and the rapid improvement of computer’s computing power, data mining (DM) came into existence. Research on DM algorithms includes knowledge of various fields such as database, statistics, pattern recognition and artificial intelligence. Pattern recognition mainly extracts features of known data samples. The DM algorithm using pattern recognition technology is a better method to obtain effective information from massive data, thus providing decision support, and has a good application prospect. Support vector machine (SVM) is a new pattern recognition algorithm proposed in recent years, which avoids dimension disaster by dimensioning and linearization. Based on this, this paper studies the DM algorithm based on pattern recognition, and proposes a DM algorithm based on SVM. The algorithm divides the vector of the SV set into two different types and iterates through multiple iterations to obtain a classifier that converges to the final result. Finally, through the cross-validation simulation experiment, the results show that the DM algorithm based on pattern recognition can effectively reduce the training time and solve the mining problem of massive data. The results show that the algorithm has certain rationality and feasibility.

Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 842-845 ◽  
Author(s):  
Jia Mei Guo ◽  
Yin Xiang Pei
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Clustering Algorithm ◽  
Original Data ◽  
Data Set ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Redundant Data ◽  
Fuzzy Partitions ◽  
Rules Extraction

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.

Study on Application of Data Mining Technology in Syndrome Differentiation of TCM

2013 ◽  
Vol 765-767 ◽  
pp. 282-285
Author(s):  
Zhi Guo Dai ◽  
Yang Yang Han
Keyword(s):  
Data Mining ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Syndrome Differentiation ◽  
Rule Mining ◽  
Mining Technology ◽  
Disease Symptoms

Study on the applications of association rule mining in traditional Chinese medicine (TCM) knowledge and experience is carried out in this paper. The association rules of disease symptoms and syndrome differentiation, syndrome differentiation and prescription, disease symptoms and prescription are mined by analyzing the cases of patients with chronic gastritis, and then the mined association rules are interpreted that provide the beneficial reference for data mining technology in TCM.

scholarly journals Association Rules Analysis on FP-Growth Method in Predicting Sales

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Mesran Mesran ◽  
Andre Hasudungan Lubis ◽  
Ali Ikhwan ◽  
Supiyandi
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Frequent Itemset ◽  
Frequent Pattern ◽  
Data Set ◽  
Pattern Processing ◽  
Large Databases ◽  
Growth Method ◽  
Association Rules Analysis ◽  
A Company

Sales transaction data on a company will continue to increase day by day. Large amounts of data can be problematic for a company if it is not managed properly. Data mining is a field of science that unifies techniques from machine learning, pattern processing, statistics, databases, and visualization to handle the problem of retrieving information from large databases. The relationship sought in data mining can be a relationship between two or more in one dimension. The algorithm included in association rules in data mining is the Frequent Pattern Growth (FP-Growth) algorithm is one of the alternatives that can be used to determine the most frequent itemset in a data set.

scholarly journals Design of Macroeconomic Growth Prediction Algorithm Based on Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongxiang Sun ◽  
Zhongkai Yao ◽  
Qingchun Miao
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Association Rules ◽  
Rapid Development ◽  
Financial Data ◽  
Prediction Algorithm ◽  
Economic Data ◽  
Growth Prediction ◽  
Macroeconomic Time Series ◽  
Macroeconomic Growth

With the rapid development of information technology and globalization of economy, financial data are being generated and collected at an unprecedented rate. Consequently, there has been a dire need of automated methods for effective and proficient utilization of a substantial amount of financial data to help in investment planning and decision-making. Data mining methods have been employed to discover hidden patterns and estimate future tendencies in financial markets. In this article, an improved macroeconomic growth prediction algorithm based on data mining and fuzzy correlation analysis is presented. This study analyzes the sequence of economic characteristics, reorganizes the spatial structure of economic characteristics, and integrates the statistical information of economic data. Using the optimized Apriori algorithm, the association rules between macroeconomic data are generated. Distinct features are extracted according to association rules using the joint distribution characteristic quantity of macroeconomic time series. Moreover, the Doppler parameter of macroeconomic time series growth prediction is calculated, and the residual analysis method of the regression model is used to predict the growth of macroeconomic data. Experimental results show that the proposed algorithm has better adaptability, less computation time, and higher prediction accuracy of economic data mining.

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