Rule Algorithm Research Articles

Brand Marketing Leveraging the Advantage of Emoji Pack Relying on Association Rule Algorithm in Data Mining Technology.

The random and disordered configuration of expression package image, text, and context is in line with the emotional consumption trend of young groups, so it has become a kind of image symbol favored by young groups. How to integrate the text and images related to emoticons and carry out emotion analysis in a specific consumer environment is the focus of attention. In view of these needs and defects, this study uses the association rule algorithm in data mining technology to explore brand marketing targeting different consumers. Taking the nonverbal symbols on social media as the research object, this study collects and mines the download ranking and appreciation of the expression package of “WeChat expression open platform.” The persistence of users' use of expression package has the characteristics of power distribution. When choosing, there are sprouting attributes and dynamic and serialized selection preferences. The advantages of emoticon package are used, brand communication and marketing are strengthened, and effective brand marketing analysis is implemented. The simulation results show that by taking advantage of the strong communication and appeal of the expression package, the brand will help to expand the contact surface of the marketing brand concept. Secondly, as a gathering community, expression packs are divided into different circle groups between and within groups, which helps brand marketing realize precision marketing based on “strong connection” with the help of expression packs and promote the occurrence of consumer behavior.

Application of Internet of Things Technology in Student Management Evaluation System

In modern society, the rapid development of the knowledge economy makes education become the core resource of a country’s economic and social modernization development. Under this circumstance, student management has been paid more and more attention by people. This paper aims to study how to use the internet of things technology to study the student management evaluation system. This paper proposes a data mining algorithm based on the internet of things technology and proposes a decision tree method and an association rule algorithm based on the data mining algorithm. The experimental results of this paper show that the student growth rate in 2018 increased from 10% to 33% in 2019 and then from 21% in 2020 to 35% in 2021. It can be seen that with the development of the economy, the enrollment rate is also increasing, and the number of people who can go to college has increased rapidly. This also increases the difficulty of student management, making teachers’ work more arduous, and the complexity of the amount of information leads to a drop in the efficiency of student management. However, the student management evaluation system based on the internet of things technology is beneficial to solving this problem.

Performance Evaluation of Sequential Rule Mining Algorithms

Data mining techniques are useful in discovering hidden knowledge from large databases. One of its common techniques is sequential rule mining. A sequential rule (SR) helps in finding all sequential rules that achieved support and confidence threshold for help in prediction. It is an alternative to sequential pattern mining in that it takes the probability of the following patterns into account. In this paper, we address the preferable utilization of sequential rule mining algorithms by applying them to databases with different features for improving the efficiency in different fields of application. The three compared algorithms are the TRuleGrowth algorithm, which is an extension sequential rule algorithm of RuleGrowth; the top-k non-redundant sequential rules algorithm (TNS); and a non-redundant dynamic bit vector (NRD-DBV). The analysis compares the three algorithms regarding the run time, the number of produced rules, and the used memory to nominate which of them is best suited in prediction. Additionally, it explores the most suitable applications for each algorithm to improve the efficiency. The experimental results proved that the performance of the algorithms appears related to the dataset characteristics. It has been demonstrated that altering the window size constraint, determining the number of created rules, or changing the value of the minSup threshold can reduce execution time and control the number of valid rules generated.

Extraction of Key Factors of Ship Following Behavior based on Rough Set Theory

In order to clarify the main influencing factors of ship following behavior, an algorithm for extracting key factors of ship following behavior based on ship AIS data in straight channel waters is proposed. After the statistical analysis of the obtained ship following trajectory data, the correlation of each influencing factor of ship following behavior and the importance of their influence on the ship following distance are analyzed by applying the rough set dependency and importance assignment methods, and the attribute simplification algorithm based on the rough set dependency and the decision rule algorithm based on the rough set attribute simplification are used to mine the strong correlation rules between each influencing factor and the following distance. Finally, set the straight section of the south channel of the Yangtze River estuary as the study water for case study. The results show that the average ship speed is 9.41 knots, the average space of ship is 2110 m, the average bow time is 450s, and the average relative speed is 0 knot; the weights of ship speed, ship length, relative speed, ship type, density and pilot class in the factors affecting ship following behavior are 0.1953, 0.0836, 0.2092, 0.2265, 0.1852 and 0.1058, respectively; the correlation between ship speed and relative speed is large, no strong correlation rule was found with others; ship following behavior should focus on ship speed and density, and the distance of [1000, 2000 m) is the key concern interval.

Live Multiattribute Data Mining and Penalty Decision-Making in Basketball Games Based on the Apriori Algorithm.

The Apriori algorithm is used to conduct an in-depth analysis and research on the relationship between data mining and penalty decision of multiattribute data in the basketball game scene. The technical and tactical features are analyzed using an improved Apriori algorithm for association rule analysis of basketball game data. The algorithm generates association rules based on mining the set of frequent items among basketball technical actions. The improved algorithm can mine the technical moves that are more connected in the game data, and the analysis results are highly instructive. The technical and tactical directed analysis is divided into two parts: technical and tactical directed action analysis and technical and tactical directed cooperation analysis. The key action analysis uses Markov process-based data mining algorithm to analyze the basketball game data for key score transfer steps and key score loss transfer steps. The algorithm can find the key actions of scoring and key actions of conceding points in the game process, and the analysis results can guide basketball training and games, which has high practical value. Using the collated game data as the independent variable and the number of games won and lost as the dependent variable, logistic regression analysis is applied to derive the characteristics that affect winning. Again, the decision tree algorithm is used to select the significant features that affect winning and to make predictions of team performance. Finally, the technical statistics of the main players in the last three seasons are selected, and the association rule algorithm is applied to derive the degree of influence of player performance on the outcome of the game.

Financial Risk Assessment of Enterprise Management Accounting Based on Association Rule Algorithm under the Background of Big Data

Under the background of the rapid development of data informatization, the evaluation and analysis of enterprise management accounting risk are the key link of enterprise sustainable development. Based on the theoretical basis of enterprise management accounting risk analysis, this paper selects 11 representative corporate financial risk indicators from financial risk indicators through correlation analysis and then selects 8 key indicators that affect corporate financial risk through association rules, to assess and analyze the company’s financial risks. Finally, this paper takes 20 ST companies in China as the research object to carry out empirical research. According to the assessment and analysis of financial risks in the company’s management accounting, it proposes measures from four modules to strengthen the company’s ability to resist risks.

Research on the Characteristic Model of Learners in Modern Distance Music Classroom Based on Big Data

This paper makes in-depth research on data mining, especially association rule mining, improves the FP-tree algorithm in both the algorithm itself and the data source, and finds out a mining algorithm suitable for learner characteristics. Association rule algorithm for actor feature model mining. By establishing the characteristic model of learners in modern distance music classroom, simulation experiments are carried out on FP-tree and three improved algorithms. This paper improves the FP-tree algorithm. Firstly, we improve the algorithm itself; aiming at the problem of too many frequent itemsets, an improved key item extraction algorithm KEFP-growth based on FP-growth is proposed, which ignores the frequent itemsets that are not concerned in the analysis. Then, improvements were made in terms of data sources. In view of the problem that the data source is too large, the mining efficiency is low, and the FP-tree cannot be loaded in memory, this paper proposes a data projection algorithm, which adopts the idea of divide and conquer, divides the frequent 1-itemsets of the database into database subsets of each frequent 1-itemsets, and then mines the database subsets separately and then merges them. Finally, the KEFP-growth algorithm and the projection algorithm are combined, which can not only eliminate the mining of meaningless frequent items but also divide the data when there is a large amount of data. This paper also compares the performance of the three improved algorithms and the original FP-tree algorithm through experiments. The experiments show that the combination of the KEFP-growth algorithm and the database projection algorithm is the most suitable one for the learner feature mining of the adaptive learning system. (1) The KEFP-growth algorithm reduces the number of frequent items output by the original FP-tree algorithm by about 50%, and the mining time is reduced by 50%. (2) The data projection algorithm is more suitable for data mining with less support. When the support is 10%, the mining time of the data projection algorithm is reduced by 80% compared with the FP-tree algorithm. (3) When the support degree is 10%, the running time of the hybrid algorithm is reduced by 10% compared with the KEFP-growth algorithm and the data projection algorithm.

A Quality Control Method and Implementation Process of Wind Profiler Radar Data

Wind profiler radar (WPR) is used for all-weather atmospheric wind-field monitoring. However, the reliability of these observations reduces significantly when there is electromagnetic interference echo, generally caused by ground objects, birds, or rain. Therefore, to optimize the data reliability of WPR, we proposed a synthetic data quality control process. The process included the application of a minimum connection method, judgment rule, and median test optimization algorithm for optimizing clutter suppression, spectral peak symmetry detection, and radial speed, respectively. We collected the base data from a radiosonde and multiple radars and conducted an experiment using these data and algorithms. The results indicated that the quality control method: (1) had good adaptability to multiple WPRs both in clear sky and precipitation; (2) was useful for suppressing ground clutter and (3) was superior to those of the manufacturer as a whole. Thus, the data quality control method proposed in this study can improve the accuracy and reliability of WPR products and multiple types of WPR, even when they function under vastly different weather conditions.

An Informatization Model of Scientific Computing for Mining Association Rules Used in Teaching Management Evaluation

The management information system is a kind of information-based, people-oriented, and socialized service as its purpose. It adopts advanced technology to realize the management of teachers, students, and other related personnel while effectively improving the quality of teaching. This article first analyzes the overview of data mining technology. Secondly, it analyzes the related algorithms of association rules. Finally, this article introduces the demand analysis of the university teaching quality evaluation system based on the scientific calculation of association rules. According to the demand analysis results, the system module and the corresponding submodule function modules are determined, and finally, the system architecture model is designed. Experimental results show that this model is useful for providing the effectiveness of information management.

Customer Relationship Management Based on SPRINT Classification Algorithm under Data Mining Technology.

Under the advance of computational intelligence, customer relationship management system based on data mining technology can not only bring more economic benefits to an enterprise but also improve the management and decision-making level of Chinese enterprises. In this paper, the application of data mining technology in customer relationship management (CRM) is analyzed, and four data mining modes are realized: customer classification, cross-marketing, customer acquisition, and customer retention. In the data mining module, SPRINT classification algorithm is used in customer classification. At the same time, FP-growth, an association rule algorithm without candidate set, is applied in cross-marketing, which enhances the practicability of the system. The algorithm of optimal customer retention strategy under digital intelligence technology is adopted in customer retention, which makes up for the shortcomings of traditional CRM system and helps enterprises to better operate and adjust marketing strategies.

Construction Principles of Physical Fitness Training Objective System Based on Machine Learning and Data Mining

The objective is to address the issue of simplification of physical education classes offered by large colleges and universities. The evaluation standard of physical education curriculum is not unified. The physical education management system focuses on the functions of collecting information, sorting, and statistics and has low timeliness and guiding significance. This paper puts forward an analysis of the construction principle of physical fitness training target system based on machine learning and data mining. This paper uses informational analysis to statistically analyze the healthy behavior of college students, to guide the physical education of college students, and to propose a model for the analysis of healthy sports behavior of college students based on data mining technology. Create a decision tree template for students whose cardiovascular function does not meet the standard using the decision tree algorithm. The association rule algorithm is used to mine the association of five indexes of physical health, so as to judge the hidden law between students’ physical fitness and behavior habits and get the correlation information of various physical health indicators. The simulation results show that, through the prediction of college students’ healthy sports behavior data, when the sample point is 5, the original value data is 16, which is higher than the estimated value, the convergence of the overall data characteristic distribution is good, and the disturbance error is low. Therefore, using this method to analyze the application of college students’ healthy sports behavior has a high accuracy of sports-related data mining and can effectively guide college students’ sports management and training.

Real Estate Marketing Adaptive Decision-Making Algorithm Based on Big Data Analysis

Aiming at the problems of low stability and efficiency of marketing decision-making and large complexity of marketing decision-making in the current marketing adaptive decision-making algorithm, a real estate marketing adaptive decision-making algorithm based on Big Data analysis is proposed. By analyzing the concept of Big Data, using the Big Data distributed computing architecture, researching the data mining-related algorithms. By constructing an association rule algorithm, mining the rules between real estate marketing and related factors. Based on the Spark-distributed computing platform, an optimization idea of association mining is designed. Decision tree algorithm is used to select discrete and continuous attribute features. According to the characteristics of real estate marketing data and the weight-based discrimination method, the decision tree pruning algorithm is optimized using the classification accuracy, stability, and complexity criteria, and the adaptive decision-making model of real estate marketing is constructed to realize the adaptive decision-making of real estate marketing. The experimental results show that the proposed algorithm has high stability and efficiency in real estate marketing adaptive decision-making and can effectively reduce the complexity of marketing decision-making.

Determination of the world stock indices' co-movements by association rule mining

PurposeThis study aims to provide preliminary information to the investor by determining which indices co-movement, with the data mining method.Design/methodology/approachIn this context, data sets containing daily opening and closing prices between 2001 and 2019 have been created for 11 stock market indexes in the world. The association rule algorithm, one of the data mining techniques, is used in the analysis of the data.FindingsIt is observed that the US stock market indices take part in the highest confidence levels between association rules. The XU100 stock index co-movement with both the European stock market indices and the US stock indices. In addition, the Hang Seng Index (HSI) (Hong Kong) takes part in the association rules of all stock market indices.Originality/valueThe important issue for data sets is that the opening/closing values of the same day or the previous day are taken into account according to the open or closed status of other stock market indices by taking the opening time of the stock exchange index to be created. Therefore, data sets are arranged for each stock market index, separately. As a result of this data set arranging process, it is possible to find out co-movements of the stock market indexes. It is proof that the world stock indices have co-movement, and this continues as a cycle.

Application of Algorithm of Fuzzy Rule Conclusions in Determination of Animal’s Diseases

Today, the detection of non-communicable animal diseases, the analysis of experimental data and the construction of a mathematical model is one of the main problems. In the article a method for determining the type of disease in cattle using a fuzzy inference rule algorithm is discussed. In our country, diseases with ketosis, microelementosis, ostradistraphia and secondary ostradithraphy in livestock, especially cattle, are determined by errors in determining the type of disease due to their similar symptoms. Since the symptoms of these diseases are interrelated and one of them can cause the other, solving this problem using fuzzy rule algorithms helps to reduce the error in determining the type of disease.

Determining customer limits by data mining methods in credit allocation process

The demand for credit is increasing constantly. Banks are looking for various methods of credit evaluation that provide the most accurate results in a shorter period in order to minimize their rising risks. This study focuses on various methods that enable the banks to increase their asset quality without market loss regarding the credit allocation process. These methods enable the automatic evaluation of loan applications in line with the sector practices, and enable determination of credit policies/strategies based on actual needs. Within the scope of this study, the relationship between the predetermined attributes and the credit limit outputs are analyzed by using a sample data set of consumer loans. Random forest (RF), sequential minimal optimization (SMO), PART, decision table (DT), J48, multilayer perceptron(MP), JRip, naïve Bayes (NB), one rule (OneR) and zero rule (ZeroR) algorithms were used in this process. As a result of this analysis, SMO, PART and random forest algorithms are the top three approaches for determining customer credit limits.

Construction and Application Analysis of University Management System Based on Association Rule Mining Algorithm Apriori

With the rapid development of data mining and dynamic data modeling, this technology has been applied in most groups. In recent years, with the continuous development of the data processing technology, society has entered the digital era. Big data technology is widely used in economics, medicine, computer, and other fields. Based on the development status of the big data technology, an association rule mining algorithm Apriori is proposed to study the characteristics of massive data in a university management system. Combined with the big data discrete dynamic modeling technology, the cloud storage problem in the management system is optimized and improved. Dynamic modeling technology is used to optimize the energy-saving and energy storage functions of the system. Secondly, the Apriori algorithm is used to mine the student achievement data, and the factors affecting the change of student achievement are analyzed. Finally, the running efficiency of the whole university management system is dynamically modeled and analyzed. The results show that the system performance of Apriori algorithm mining has high client compatibility. Association rule algorithm is mainly used in student personality analysis and personal information management, The algorithm used in this paper can optimize the overall performance of the university management system and has certain effectiveness and applicability. It can quickly query the required information under the background of big data and has strong applicability. Dynamic modeling and optimization of the storage system can also improve the utilization of the storage system.

Comparison of discretization methods for classifier decision trees and decision rules on medical data sets

Data sets in real life are given by real numbers in databases. On the other hand, many data mining methods like association rules and induction rules require only discrete attributes. For this reason, it is necessary to convert data sets with continuous attributes into data sets with discrete attributes. Discretization process is reducing the number of values for a given continuous attribute by dividing the range of the attribute into intervals. In this paper, eight discretization methods are presented with JRip, OneR, J48, and Part classifier algorithms of rules and tress. Experiments include a ten-fold cross validation provided on the basis of real-life data sets from the UCI repository. We show that discretization is important step to significantly increase the classification results of these algorithms. Finally, as a result of the study, it was seen that MDL and J48, CAIM and Jrip and Extended Chi and J48 methods gave the highest accuracy for PIMA, WBC and DERMA data sets, respectively.

Development of a Reservoir Flood Control Scheme for Global Flood Models

AbstractIntegrating reservoir flood control operations in global flood forecasting systems is important for accurately estimating discharge and other variables. Because existing modeling operational rules and parameters do not reflect the actual variability due to a lack of associated data, globally applicable modeling of flood regulation needs to be studied further. In this study, we developed a flood control operation scheme with refined parameters and algorithms to tackle this problem. We used recently developed objective data sets of reservoir dynamics to more realistically estimate different reservoir parameters, such as flood storage capacity. Furthermore, the algorithm for the operational rule was modified by introducing a new release coefficient such that the peak attenuation depended on the reservoir's ability to regulate floods. The operation scheme for 2,169 dams was introduced into the CaMa‐Flood global hydrodynamic model. Compared to the simulation without reservoirs, the Nash‐Sutcliffe Efficiency and peak discharge error in the daily river discharge improved at 62.2% and 49.9% of the gauges, respectively, after the integration. Compared to the parameter estimated by a method in previous studies, the newly estimated flood storage capacity parameters reduced the peak discharge error at 47% of the gauges downstream of the reservoirs where the flood storage capacity ratio is small (≤50%). Furthermore, the modification of operational rules had the effect of mitigating people exposed to flooding by ∼10%. This study informs the integration and the refinements of the reservoir operation scheme to improve global flood simulation and forecasting.

Design of the Higher Education System Based on Parallel Association Rules Algorithm

Aiming at improving the quality of online education, a higher education system based on parallel association rules algorithm is designed. In this study, the functional structure module of the system is divided into six modules, namely, the home page module, course module, teacher module, student module, administrator module, and personal center, so as to carry out a comprehensive treatment for students, teaching, and education resources. On the basis of mining the data association rules of the education system, the parallel association rules algorithm is used to identify and analyze the original data of the system, so as to fundamentally improve the ability of the system to process data and complete the personalized recommendation of educational resources and teaching evaluation feedback. Experiments show that the design system has greater information throughput, a short response time, and a resource utilization rate of more than 90%. In addition, the audio teaching resource plays better, which proves that the system effectively achieves the design expectation.

BPR algorithm: New broken plural rules for an Arabic stemmer

One of the most important phases in text processing is stemming, whose aim is to aggregate all variations in a word into one group to aid natural language processing. The morphological structure of the Arabic language is more challenging than that of the English language; thus, it requires superior stemming algorithms for Arabic stemmers to be effective. One of the challenges is the irregular broken plural, which has been a problematic issue in Arabic natural language processing that affects the performance of Arabic information retrieval and other Arabic language engineering applications. Several studies have attempted to develop solutions to irregular plural problems, but the challenge remains, especially in extracting correct Arabic root words. In this paper, the broken plural rule (BPR) algorithm introduces new solutions to solve the problem in which an existing root-based method cannot extract correct roots by using their proposed rules. The BPR algorithm introduces several rules (main rules and subrules) to extract the correct roots of the Arabic irregular broken plural words. To evaluate the effectiveness of the BPR algorithm, we extracted roots from an Arabic standard dataset and applied the BPR algorithm as an enhancement to a root-based Arabic stemmer, ISRI. The obtained results from both evaluations showed encouraging results: (i) Only a few numbers of incorrect roots were stemmed on the large-sized Arabic word dataset. (ii) The enhanced root-based Arabic stemmer, ISRI + BPR, exhibited the best performance compared with the original ISRI stemmer and a well-known Arabic stemmer, ARLS 2. Thus, the proposed BPR algorithm has solved some of the irregular broken plural problems that eventually increase the performance of a root-based Arabic stemmer.