Data Mining Functions Research Articles

Application analysis of data mining in shopping APP

Dingdong Buying Vegetable is a cohort of emerging entities that have swiftly gained prominence within the business domain in recent times. The data mining function of its data platform APP is undeniably linked to the underlying factor contributing to its commercial success. This article examines the fundamental principles, practical manifestations, and pertinent research instances of data mining. It specifically centers on the primary interface of the Dingdong Buying Vegetable APP, scrutinizing its design and distinctive attributes tailored to specific customers. Furthermore, it conducts an in-depth analysis of the correlation between the platforms commercial success and its data mining functionality. The primary aspect in which the data mining function of the Dingdong Buying Vegetable APP is expected to be manifested is through extensive data mining. The process of mining client data and conducting a full comparison and analysis of sales data for all products sold is a highly intricate and exhaustive endeavor. The use of these data mining functions serves multiple purposes. Firstly, it efficiently identifies customers genuine requirements on the client side, enabling the recommendation of suitable products and fostering customer reliance on the application. Additionally, it facilitates accurate decision-making in marketing products on the product side by comparing and analyzing diverse data pertaining to the sold products. This approach helps prevent the sale of unsought items and effectively minimizes company expenditure.

The Impact of Big Data-Driven Industrial Digital Unification System on Commercial Management Operational Efficiency

Abstract Integrated management digitization is an important way to boost the competitiveness of commercial management enterprises. In this paper, we first design the functional architecture and system deployment of the commercial management enterprise digital system, including digital operation management, commercial operation data mining analysis, and other functional modules to meet the commercial operation management needs of commercial complexes, office buildings, and urban integrated operation business. The K-means clustering algorithm is then improved by using a particle swarm algorithm that is based on it. Specifically, the distribution estimation algorithm and stagnation perturbation strategy are used to update the population information and control the particle position boundary. Then, the greedy approach is used to select the advantageous particles. Then, the digital unified construction system of the commercial management enterprise is finally constructed to realize the data mining function of the operation and management and to assist the enterprise in carrying out customer management, property rights management, scientific decision-making, risk assessment, and so on. Finally, after testing the data analysis performance of the system, the business performance of the W commercial management group company that uses the system of this paper for digital transformation is analyzed. It is found that the accuracy of this paper's algorithm is the same as the PSO-Kmeans method. Still, the number of iterations of this paper's algorithm is the least; the lowest is only 14 times, and the optimization of efficiency is significant. The return on net assets and the net sales margin of W Commercial Management improved from 15.8% and 28.12% in 2016 to 20.20% and 28.12% in 2023. The debt repayment and operation ability are also optimized substantially, and the system designed in this paper The effectiveness of the developed system is proved. This study provides a proven solution for the digital transformation of commercial management enterprises and improves the operational efficiency of commercial management.

A cluster and association analysis visualization using Moodle activity log data

The course activity log is where a learning management system (LMS) like Moodle keeps track of the various learning activities. In order to conduct a quicker and more in-depth examination of the students' behaviors, the instructor may either directly examine the log or make use of more complex methodologies such as data mining. The majority of the proposed methods for analyzing this log data center mostly on predictive analysis. In this research, cluster analysis and association analysis, two separate data mining functions, are investigated in order to analyze the log. The students' activities are used in the cluster analysis performed with K-Means++, and the association analysis performed with Apriori is used to investigate the connections between the students' various activities. A dashboard presentation of the findings is provided in order to facilitate clearer comprehension. Based on the findings of the analysis, it can be concluded that the structure of the student cluster is medium, whereas the association between the activities undertaken by students is positively correlated and well-balanced. The subjective review of the dashboard reveals that the visualization is already sufficient, but there are some recommendations for making it even better.

Building a Platform for Data Mining in a Big Data Environment Combining R-language with Hadoop

With the exponential growth of data in various industries, the need for effective data mining platforms has become crucial. This research focuses on constructing a data mining platform suitable for processing large-scale and diverse data sets. The platform leverages the power of the R language and utilizes the scalability and processing capabilities of Hadoop. The system architecture encompasses a physical layer, virtualization layer, service layer, and application layer. Heterogeneous hardware resources are deployed at the physical layer, while virtual machines are created and managed using CloudStack at the virtualization layer. The service layer integrates the R language, enabling the implementation of various data mining functions. Finally, the application layer provides users with a user-friendly interface to customize flow paths and configure parameters. The proposed method effectively processes big data, facilitates comprehensive data analysis, and achieves high processing efficiency.

Kernel Partial Least Squares Feature Selection Based on Maximum Weight Minimum Redundancy

Feature selection refers to a vital function in machine learning and data mining. The maximum weight minimum redundancy feature selection method not only considers the importance of features but also reduces the redundancy among features. However, the characteristics of various datasets are not identical, and thus the feature selection method should have different feature evaluation criteria for all datasets. Additionally, high-dimensional data analysis poses a challenge to enhancing the classification performance of the different feature selection methods. This study presents a kernel partial least squares feature selection method on the basis of the enhanced maximum weight minimum redundancy algorithm to simplify the calculation and improve the classification accuracy of high-dimensional datasets. By introducing a weight factor, the correlation between the maximum weight and the minimum redundancy in the evaluation criterion can be adjusted to develop an improved maximum weight minimum redundancy method. In this study, the proposed KPLS feature selection method considers the redundancy between the features and the feature weighting between any feature and a class label in different datasets. Moreover, the feature selection method proposed in this study has been tested regarding its classification accuracy on data containing noise and several datasets. The experimental findings achieved using different datasets explore the feasibility and effectiveness of the proposed method which can select an optimal feature subset and obtain great classification performance based on three different metrics when compared with other feature selection methods.

Evaluation Method of Product Shape Features based on Multidimension Spatial Data Mining

Analysis of product shape design has attracted more and more attention of researchers because it can make products better meet perceptual needs. Based on the theory of spatial data mining, this study proposes an evaluation method of a product shape design scheme. The method proposed in this study not only obtains the shape design of the overall and local features of the product, which solves the problem of the insufficient utilization of spatial data by the analysis method. During the simulation process, the model obtains the product shape design and appeal from the online evaluation spatial data, which can integrate the product perceptual knowledge in the spatial data, which greatly reduces the manual operation steps and the required time for the degree of data utilization. The experimental results show that after the obtained data are filtered to extract the feature words, the weight of the feature words is calculated by the TF-IDF method, the number of neighbors is increased from 1 to 30, the interval is 5, and the vectorized representation of the spatial data is constructed. The similarity between the calculation sentences of the data mining method is 89.7%, which effectively improves the support function and design efficiency of spatial data mining for product design.

Analysis of Medication Rule of Primary Epilepsy Based on Xiaocheng Yan's Clinical Experience Collection of Epilepsy.

Objective To explore and analyze the medication rule of Professor Xiaocheng Yan in the treatment of primary epilepsy, hoping to provide reference for the clinical treatment of primary epilepsy. Methods Mining and analysis of Professor Xiaocheng Yan sorted out the medical cases of primary epilepsy in Xiaocheng Yan's clinical experience collection of epilepsy, extracted the traditional Chinese medicine (TCM) prescription data in the medical cases, standardized the obtained TCM prescription data, and used the data mining function integrated by the ancient and modern medical case cloud platform V2.3.5 to carry out frequency statistics, cluster analysis, association analysis, and complex network analysis on the TCM data, and the common herbs used by Professor Xiaocheng Yan in the treatment of primary epilepsy, properties and classifications of commonly used herbs, pairs of commonly used herbs, and core prescriptions were obtained. Results A total of 39 cases, 228 medical records, and 230 prescriptions data of TCM were included. A total of 96 Chinese medicinal herbs were involved, and the total frequency of medication was 3,828. High-frequency herbs include Rhizoma Gastrodiae (Tianma) (222 times), Ramulus Uncariae cum Uncis (Gouteng) (220 times), Rhizoma Acori Tatarinowii (Shichangpu) (216 times), Rhizoma Pinelliae Praeparatum (Fabanxia) (207 times), Bombyx Batryticatus (Jiangcan) (206 times), and Periostracum Cicadae (Chantui) (181 times). The main properties and flavors of commonly used Chinese medicinal herbs were sweet, bitter, and pungent, which were mainly attributed to the four meridians of liver, lung, heart, and spleen. Commonly used couplet herbs were {Periostracum Cicadae (Chantui)} ≥ {Bombyx Batryticatus (Jiangcan)}, {Rhizoma Acori Tatarinowii (Shichangpu)} ≥{ Bombyx Batryticatus (Jiangcan)}, {Radix Bupleuri (Chaihu)} ≥ {Radix Scutellariae (Huangqin)}, {Rhizoma Gastrodiae (Tianma)} ≥ {Ramulus Uncariae cum Uncis (Gouteng)}, {Rhizoma Acori Tatarinowii (Shichangpu)} ≥ {Periostracum Cicadae (Chantui)}, {Ramulus Uncariae cum Uncis (Gouteng)} ≥ {Bombyx Batryticatus (Jiangcan)}, {Bombyx Batryticatus (Jiangcan)} ≥ {Rhizoma Gastrodiae (Tianma)}, {Rhizoma Acori Tatarinowii (Shichangpu)} ≥ {Ramulus Uncariae cum Uncis (Gouteng)}, etc. The core prescription composition was based on the addition and subtraction of Tianma Gouteng decoction and Erchen decoction. The main pharmacological mechanisms of core prescriptions are mainly reflected in antioxidation, enhancing GABA efficacy, and regulating NMDA channel and sodium channel, neuroprotection, and so on. Conclusion Professor Xiaocheng Yan's medication for the treatment of primary epilepsy was based on the principle of relieving wind and spasm, drying dampness and resolving phlegm, giving consideration to both Qi and blood, and harmonizing liver, lung, heart, and spleen.

Construction of College Aesthetic Education Quality Improvement Model under Big Data Network

In view of the importance and urgency of college aesthetic education, this paper constructs a quality improvement model of college aesthetic education under the big data network. Firstly, it puts forward the characteristics of planning, step-by-step, scientific, and centralized aesthetic education in colleges and universities and then expounds the importance of aesthetic education in colleges and universities, including the importance of aesthetic education in spiritual education and in the construction of socialist spiritual civilization. Integrating the functions of data mining under the big data network, using the analytic hierarchy process to construct the improvement index, and carrying out the consistency test at the same time, we can get the improvement index system of aesthetic education quality in colleges and universities. The fuzzy judgment matrix is constructed to obtain the final improvement result. The experimental results show that the constructed model verifies the importance and urgency of aesthetic education in colleges and universities, has a high degree of freedom, and reduces the complexity of quality improvement.

Application of Decision Tree Algorithm Based on Data Mining in English Teaching Evaluation

In order to bring new opportunities and challenges to teaching and learning English. This paper proposes a study of the use of a data tree-based decision tree algorithm in English language learning assessment. Teaching and learning through online English learning platforms using data mining technology to analyze student learning data, create relevant models, and explore the relationship between English exams and various elements is important for student learning and teacher teaching. Firstly, this paper introduces the function, process, and common machine learning models of data mining. Finally, by using data created by college English tutors and measurements to create and identify student images, through image users, you can learn about student behavior, learning behaviors, etc., provide a foundation after the design experiment, technology training technology in logistic regression model, wooden model making, and postfusion modeling of two models and analyze the factors affecting students’ passing the exam according to the prediction results. The results show that the decision tree model predicts that the score of each question type occurs 72 times, the score of students’ examination occurs 70 times, and the completion of homework occurs 70 times. Trees determined by this approach can have a profound impact on a wide range of indicators of academic excellence and provide a fundamental and useful basis for measuring future improvement.

Improved Decission Tree Performance using Information Gain for Classification of Covid-19 Survillance Datasets

<p class="AbstractL-MAG">One of the most feared infectious diseases today is COVID-19. The transmission of this disease is quite fast. Patients also sometimes do not have the same symptoms. Overcoming the spread of the pandemic has been widely carried out throughout the world. Apart from the medical method, there are also many other methods, including computerization. Data mining is a discipline that can project data into new knowledge. One of the main functions of data mining is classification. Decision tree is one of the best models to solve classification problems. The number of data attributes can affect the performance of an algorithm. This study uses information gain to select the attribute features of the Covid-19 surveillance dataset. This study proves that there is an increase in the accuracy of the decision tree algorithm by adding information gain feature selection. Previously, the decision tree only had an accuracy rate of 65% for the classification of the Covid-19 surveillance dataset. After pre-processing using information gain, the accuracy rate increased to 75%.</p>

Research on Aided Judgment of Rural Sports Posture Based on Deep Learning

With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports auxiliary training, to get rid of the state of traditional sports training purely relying on experience. The article proposes a deep learning BP neural network human body posture recognition algorithm and briefly introduces the human body motion posture. The purpose of this research was to use the powerful data processing, mining, and analysis functions of deep learning to train the massive data generated in competitive sports training and apply it to competitive sports training. It is committed to promoting the accuracy and analysis of competitive sports training. Refinement provides technical guidance for athletes’ training, promotes the scientific and informatized development of competitive sports training in China, and provides some reference methods for the research and application of deep learning in competitive sports training. The article’s research results show that (1) taking a rural area as an example, we recorded the exercise postures of rural athletes in five different states: static, upstairs, downstairs, walking, and running. Comparing the recognition rate and training time of The BP neural network algorithm, ABC-BP algorithm, AFS-BP algorithm, and ABC-AFS-BP algorithm, it can be found that in terms of recognition rate, ABC-AFS-BP algorithm, AFS-BP algorithm, and ABC-BP algorithm are better than traditional BP algorithm. Among them, the recognition rate of the ABC-AFS-BP algorithm is higher than that of the ABC-BP algorithm, but it takes slightly more time than the ABC-BP algorithm. In terms of training time, the ABC-BP algorithm takes less time, but the accuracy is lower than the ABC-AFS-BP algorithm; the ABC-AFS-BP algorithm has a greater improvement in time consumption than the AFS-BP model and can guarantee the recognition rate and accuracy, and the error rate curves of the four algorithms show that after 500 iterations of the training part, the iteration error value of the ABC-AFS-BP algorithm is the smallest. (2) We evaluated sports postures of athletes from a certain rural team and concluded that bad postures will have a certain impact on the body. Among them, more than 85% of athletes in football and basketball have pelvic rotation. The problem is that football players have reached 90% of the test sample. 60% of football players and basketball players have the problem of collapsed foot. The main problem of aerobic athletes is flat back and collapsed foot. More than 90% of badminton players have high and low shoulder problems, and more than 80% of them have neck problems, which is a very serious body posture problem. (3) Detecting the flexibility experiment of the BP posture detection algorithm, compared with the traditional motion posture recording method, we tested from the three aspects of recording motion accuracy, missed detection rate, and recording time. The result shows BP posture detection. The missing detection rate of the algorithm is low, basically maintained at about 2.0, the accuracy of recording actions is relatively high, generally maintained above 98%, and the highest is 99.15%, and the recording time is short, maintained at 3–4 minutes; comparing traditional posture detection with the BP attitude detection algorithm, the missed detection rate of the algorithm is relatively high, kept at 4–6, the action accuracy is lower than that of the BP attitude detection algorithm, generally kept at about 95%, and the recording time is kept at 5–6 minutes. The posture detection algorithm is more efficient.

Research on Data Mining Algorithm Based on BP Neural Network

The current data mining algorithm has the problem of imperfect data mining function, which leads to the algorithm taking too long time. This paper designs a data mining algorithm based on BP neural network. Analyze the basic structure of the data mining algorithm, obtain the data characteristics of the multi-objective decision-making, adjust the convergence speed with the distributed computing technology to keep the inertia factor state unchanged, construct the local minimal discrete model, measure the interest of the model, calculate the optimal output value of the network using the BP (Back Propagation) neural network model, and complete the improved design of the data mining function. Experimental results: The average computational time consumption of the designed data mining algorithm is 559.827 seconds, which saves 145.975 seconds and 174.237 seconds respectively than other traditional algorithms. It is proved that the data mining algorithm based on BP neural network reduces the computational time consumption, improves the performance of data mining, and has high application value.

Research on Data Mining Algorithm Based on BP Neural Network

The current data mining algorithm has the problem of imperfect data mining function, which leads to the algorithm taking too long time. This paper designs a data mining algorithm based on BP neural network. Analyze the basic structure of the data mining algorithm, obtain the data characteristics of the multi-objective decision-making, adjust the convergence speed with the distributed computing technology to keep the inertia factor state unchanged, construct the local minimal discrete model, measure the interest of the model, calculate the optimal output value of the network using the BP (Back Propagation) neural network model, and complete the improved design of the data mining function. Experimental results: The average computational time consumption of the designed data mining algorithm is 559.827 seconds, which saves 145.975 seconds and 174.237 seconds respectively than other traditional algorithms. It is proved that the data mining algorithm based on BP neural network reduces the computational time consumption, improves the performance of data mining, and has high application value.

Application of Data Mining in Effect Evaluation of Lean Management

Quantitative evaluation is an important part of enterprise diagnosis, which promotes the scientific and modern management of enterprises. At present, the existing enterprise management evaluation methods cannot complete the mining of enterprise index data, which leads to large error and low significance coefficient in enterprise management evaluation. Therefore, the application of data mining in enterprise lean management effect evaluation is put forward. The process and main functions of data mining are analyzed; data mining algorithm is used to establish the evaluation index system of lean management effect and calculate the index weight. Using the association rules method in data mining, according to the parameters of enterprise lean management level evaluation index and weight value, through the fuzzy set transformation idea, the fuzzy boundary of each index and factor is described by the membership degree, the fuzzy judgment matrix is constructed, and the final evaluation result is obtained by multilayer compound calculation. Experimental results show that this study has a high significance coefficient, and the proposed evaluation method of enterprise lean management effect has ideal accuracy and short time consumption. In practical application, the cumulative contribution rate is higher and has higher stability.

A functionally-oriented approach to digitalization of the activities of competence centers in the field of agricultural cooperation

The publication summarizes the experience of digitalization of the activities of the competence center in the field of agricultural cooperation. The concept of digitalization of the competence center in the field of agricultural cooperation has been formulated and practically implemented, including the use of heterogeneous software products, both author’s and third-party developers. Software products and their functions that will automate the main activities of the competence center in the field of agricultural cooperation are considered. The author’s software product developed for informational and analytical support of the activities of the competence center opened at the Moscow State University of Technology and Management named after K. G. Razumovsky, allows you to automate accounting functions and data mining functions. The main functions of the created and implemented software product are accounting of agricultural producers, established cooperatives and their participation in events organized by the competence center and other organizations, accounting of grantees cooperating with the competence center, implementation of cluster data analysis methods to identify potential members of an agricultural cooperative.

Analisis Metode K-Medoids Pada Penjualan Produk Smartphone Vivo Di Kota Pematangsiantar

Globalization is a very promising era of openness and freedom, where a communication which is a means to obtain information is expected to be carried out easily and effectively. One of the communication media in question is a cellphone. The users of cellphones or cellphones used to be limited to the elite, but for now it has begun to penetrate various circles of society ranging from students, university students, civil servants and even ordinary people who have used it... K-Medoids Clustering is one of the one technique of one of the data mining functionality, the clustering algorithm is an algorithm for grouping a number of data into certain data groups (clusters). While data mining, often referred to as Knowledge Discovery In Database (KDD) is an activity that includes the collection, use of historical data to find regularities, patterns or relationships in large data sets aimed at finding out the Vivo Smartphone brand that is selling well in the market so that it can be done. early procurement. From this study, it was found that Vivo smartphones with the brand S1 Pro 8+128, V17 Pro, V19 8+128GB, V19 8+256GB, Y11 2+32GB, Y12 3+64GB, Y17 4+128GB, Y19 6+128GB are brands which sold a lot. With the results obtained can provide information to PT. Vivo is improving its service even better by increasing the stock of Vivo smartphones which are in great demand, especially in the city of Pematangsiantar

A Review on Classification of Data Imbalance using BigData

Classification is one among the data mining function that assigns items in a collection to target categories or collection of data to provide more accurate predictions and analysis. Classification using supervised learning method aims to identify the category of the class to which a new data will fall under. With the advancement of technology and increase in the generation of real-time data from various sources like Internet, IoT and Social media it needs more processing and challenging. One such challenge in processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes causing the machine learning classifiers to be more biased towards majority classes and also most classification algorithm predicts all the test data with majority classes. In this paper, the author analysis the data imbalance models using big data and classification algorithm.

Customer Segmentation using Data Mining Techinique

Data mining technique can help implementing customer relationship management dimensions namely customer identification, customer attraction, customer retention and customer development . Various Data mining functions can be used to implement CRM elements . This review paper is focused on one of the element of customer identification that is customer segmentation and its implentation using K-means clustering technique. It discuses in breif about the K-means Algorithm.

JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics

Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design. It exploits the intelligent power of machine learning methods in massive materials data from experiments or simulations to seek new materials, functionality, and principles, etc. Developing specialized facilities to generate, collect, manage, learn, and mine large-scale materials data is crucial to materials informatics. We herein developed an artificial-intelligence-aided data-driven infrastructure named Jilin Artificial-intelligence aided Materials-design Integrated Package (JAMIP), which is an open-source Python framework to meet the research requirements of computational materials informatics. It is integrated by materials production factory, high-throughput first-principles calculations engine, automatic tasks submission and monitoring progress, data extraction, management and storage system, and artificial intelligence machine learning based data mining functions. We have integrated specific features such as an inorganic crystal structure prototype database to facilitate high-throughput calculations and essential modules associated with machine learning studies of functional materials. We demonstrated how our developed code is useful in exploring materials informatics of optoelectronic semiconductors by taking halide perovskites as typical case. By obeying the principles of automation, extensibility, reliability, and intelligence, the JAMIP code is a promisingly powerful tool contributing to the fast-growing field of computational materials informatics.

The Data Mining Technique Using RapidMiner Software for New Zeotropic Refrigerant

This research presents the development of environmentally-friendly and energy efficient refrigerant for medium temperature refrigeration systems that new azeotropic refrigerant mixture of hydrofluorocarbons and hydrocarbon that can retrofit in the refrigeration system using R404A. The medium back pressure refrigeration testing standard that follow CAN/ANSI/AHRI540 standard air-conditioning, heating, and refrigeration institute (AHRI) and The properties of refrigerants and refrigeration simulation system that used national institute of standards and technology (NIST) reference fluid thermodynamic and transport properties database (REFPROP) software and NIST vapor compression cycle model accounting for refrigerant thermodynamic and transport properties (CYCLE_D-HX) software. The methodology uses decision tree function in datamining by rapid minor software that first of KDnuggets annual software poll that showed new azeotropic refrigerant mixture had cooling capacity, refrigerant effect, GWP and boiling point were lower than R404A but work and pressure for medium temperature refrigeration system of azeotropic refrigerant mixture were higher than R404A. The artificial intelligence (AI) by data mining technic can predictive environmentally-friendly and energy efficient refrigerant for medium temperature refrigeration. The result of refrigerant mixed by R134A, R32, R125 and R1270 and is consistent with the evolution of fourth-generation refrigerants that contain a mixture of HFCs and HCs which are required to produce a low-GWP, zero-ozone-depletion-potential (ODP), high-capacity, low-operating-pressure, and nontoxic refrigerant.