Data Mining Association Research Articles

Anti-Hypertension Drugs Classes of Prevention and Side Effects Diseases Efficiency Analysis in Association Rule Mining Techniques

Purpose: The Healthcare department, pharmaceutical department, Hospital and Clinical sector find out and explore the diseases of status, where it has been spreaded the communicable and non-communicable diseases among the society. The healthcare department conducting different awareness of programming about the different diseases how is affected people and how is prevented the diseases in society. Accordingly, all the healthcare awareness of information passed through the different media channel, even though high and low blood pressure is pressing public health challenges and it is recognized as the biggest contributor to the global burden of diseases. Presently people health is silently affected by blood pressure low and high level and they unable to recognize that something is amiss, high blood pressure is known as the "silent killer” and If blood pressure is excessively elevated, it may have an impact on organ damage or health issues like coronary arteries, heart valve dysfunction, diabetes, kidney diseases, heart attack and stroke this all are risk factors of blood pressure abnormal status. Hypertension diseases affected the patients need to the best prevention and feature safety. The Doctors, Pharmacist and Nurse are using Anti - hypertension drugs classes of medicines for patients. Which anti-hypertension drug classes of medicine good efficacy for patients and anti-hypertension drugs classes of medicine prevention diseases, side effects diseases knowledge is important for healthcare professional. Machine learning and Data mining knowledge discovery techniques need to understand how different classes of anti-hypertensive drugs might interact with the patient’s and medications. If the healthcare professional has access to a predictive data mining technique model, they could use this to anticipate how the patient’s condition might change over time and adjust the treatment plan proactively. This kind of analytical data mining knowledge can lead to more effective treatment and better patient outcomes. Design/Methodology/Approach: Developing machine learning concept for different anti-hypertension drugs classes of medicine efficiency analysis in hypertension prevention diseases and side effects diseases and Healthcare professional to take right decision for future adjusts treatment plan to the hypertension affected patients. Findings/Result: Orange data mining analytical tool to identify the anti-hypertension drugs classes of medicines efficacy and Doctors can take right decision to better treatment for the patients. Originality/Value: Data mining association rules of support, confidence and lift correlation analysis system helps to identified about the drug of new knowledge efficiency. Paper Type: Analytical research methods applied for analysis the different types of anti-hypertension drugs classes of association correlation efficacy in data mining machine learning system.

Apriori Algorithm Application for Consumer Purchase Patterns Analysis

The Apriori algorithm is a data mining association rule algorithm for finding relationship patterns between one or more items in a dataset. Apriori algorithm is often used in transaction data analysis or market basket analysis. Apriori algorithm is used to find out consumer purchase patterns in e-commerce systems and provide product recommendations to consumer by extaracting associations or events from transactional data. This study is purposed for deeply analyze the steps, performance of Apriori algorithm, and give relevant an example of case study to better explain the steps of Apriori algorithm application, as well as the results achieved.

Data Mining Technology in Teaching Evaluation of Colleges and Universities

Data Mining refers to the large amount of data from the database through algorithmic search reveals implicit, previously unknown and potentially valuable process information[1]. Currently, many areas during the application of data mining. Data mining association rules is one of the most important and most mature technology research methods, association rule mining can find the hidden link between the transaction and meaningful rules. The purpose of this study is to evaluate data mining techniques combined with teaching, to extract useful information from a large number of evaluation data hiding, thereby providing a basis for decision support educational administration department, improve teaching quality.

Application of Big Data Technology Combined with Clustering Algorithm in Manufacturing Production Analysis System

Production data analysis is crucial for production planning in the manufacturing industry, and accurate and a comprehensive and accurate analysis can improve production planning. To analyze the production of manufacturing industry, the research proposes the big data technology research method of the set clustering algorithm. In the process of this research method, the K-means clustering algorithm is first used to build the production measurement data system, and the Apache Hadoop Big data framework is applied. Then it introduces the Apriori algorithm for data association mining, and finally uses the genetic algorithm for production scheduling in view of big data analysis. The experiment showcases that the research method achieved a support level of 0.002 with 729 association rules when testing the number of association rules. When conducting data throughput testing, the research method achieved a data throughput of 7789 threads/s when the number of threads reached 8 in the Sleep scenario. In the analysis error testing, the error rate of the research method in the retained data fluctuates around 3.1%. When testing the number of processing operations in the process, the maximum error in the analysis results of the processing operations in the research method is 2. The results indicate that the research method possesses exceptional computational performance, carrying out manufacturing production analysis effectively and efficiently. The manufacturing production analysis system designed by the research institute offers valuable reference solutions for the informationization and intelligent development of the manufacturing industry.

SMART AND INTELLIGENT PRODUCTION STRATEGY FOR THE FLOWER MARKET USING DATA MINING KNOWLEDGE-BASED DECISION

The agriculture industry has been an enormous economic pillar in the production and consumption market value chain. The agriculture industry resets flower production factors with the agricultural technology revolution. The fastest technology provides innovative and intelligent decision-making strategies in seasonal cut flowers to increase production. This study briefs out existing farming practices, chain activity and farming technology’s significant impacts on the agriculture field and garden industry. Authors try to investigate cut flower production status and analyze production values to design innovative and intelligent strategies, especially for seasonal flower production. The study employs a flower dataset; hence, it applies floral parameter inputs and data mining association rules to create an output of the flower production category, which fits appropriately to evaluate flower market production value in a particular season. The article's result reveals that the proposed flower production strategy provides efficient and intelligent guidelines to increase flower production according to market demand. This study suggests an intelligent and friendly production strategy for gardeners that indicates the flower market gets continuous and quality production to meet consumers’ immediate market demand.

Retracted: Evaluation and Construction of College Students’ Growth and Development Index System Based on Data Association Mining and Deep Learning Model

Retracted: Evaluation and Construction of College Students’ Growth and Development Index System Based on Data Association Mining and Deep Learning Model

PENERAPAN METODE ASOSIASI DATA MINING PADA E-COMMERCE TOKO NADHIRA

The purpose of this problem is to produce e-commerce using the association data mining method with the FP-growth algorithm to determine the products that appear most frequently. It also aims to make it easier for buyers and visitors to find products that are most frequently accessed by visitors. At the Nadhira Batik shop, the sales process is conventional, namely visitors come directly to the store and sort the clothing products to be purchased for the payment process, which is also done directly to the cashier. The media used for sales are also still limited, especially for payments that cannot be made through the system. The method used is the association method with the FP-Growth algorithm while the system development method used is waterfall development with the stages of analysis, design, coding, testing and implementation. The results of this study are e-commerce using the FP-Growth data mining association algorithm to determine the products that appear most frequently.

Penerapan Data Mining Dalam Penentuan Proritas Pemesanan Produk Berdasarkan dengan Data Penjualan Barang Menggunakan Algoritma Apriori

Products cannot be separated from the buying and selling process carried out by customers and sellers. Products that are a priority are, of course, products that are favored by consumers or customers. However, companies often make mistakes in the process of determining which products to order for sale. Errors in the process of determining product orders can result in losses for the company. Therefore, it is necessary to handle this problem properly and appropriately. Therefore, the settlement that should be done should be done by processing the data on the sale of these goods. Data mining is a method that can be used for data processing. But the data processing process carried out in data mining is not done independently. The a priori algorithm is one of the algorithms found in the data mining association technique. The a priori algorithm is widely used to carry out the analysis process on sales data to find sales patterns in the company. The results of the study were 3 items that became the priority of ordering, namely ordinary pants, shorts and t-shirts. With a combination of item sets, if you buy ordinary pants, you also buy shorts with a support value of 10% and a confidence value of 20%. Other item combinations that you get: If you buy regular pants, you also buy T-shirts with a support value of 30% and a confidence value of 60% and the last combination of set items. If you buy shorts, you also buy T-shirts with a support value of 30% and a confidence value of 60%.

Penerapan Algoritma FP-Growth dalam Penentuan Promosi Kampus

Campus or college is an institution as well as an institution that is included in the education sector. Now the campus is included in a business sector that is engaged in education. Therefore, the campus is also inseparable from the promotion process. The promotion carried out by the campus has the aim of making prospective new students register on the campus. For private campuses, students are input that can be useful to continue the operational process for the campus. The problem that is still widely found at this time is the process of determining campus promotions is still based on assumptions. Where the assumptions used are not based on a fact or special reference. Determination of promotion on campus can be seen based on the data of students who have registered actively on the campus. From the student data, it can be processed based on Regional Origin, School Origin, Department and Study Program taken. Data mining is a technique used to process large amounts of data. Frequent Pattern Growth Algorithm (FP-Growth) is an algorithm that is included in the data mining association technique. The results obtained in the study that for determining the promotion strategy are if you take the Study Program = Informatics Engineering (PS1) then the Department = Science (J1) and Regional Origin = Deli Serdang (AD1) with a Support value = 30% and a Confidence value = 60% and Department = Science (J1), Regional Origin = Deli Serdang (AD1) with Support = 30% and Confidence = 75%. Based on the results of the process that was carried out, it was found that the promotion target was Deli Serdang, the Department was Science and the Study Program of interest was Informatics Engineering.

Data Mining-Based Tracking Method for Multisource Target Data of Heterogeneous Networks

In order to solve the problem that the target is easily lost in the process of multisource target data fusion tracking, a multisource target data fusion tracking method based on data mining is proposed. Multisource target data fusion tracking belongs to location level fusion. Firstly, a hybrid heterogeneous network fusion model is established, and then, data features are extracted, and a fusion source big data acquisition algorithm is designed based on compressed sensing to complete data preprocessing to reduce the amount of data acquisition. Based on data mining association multisource fusion target, get the relationship between each measurement and target, and build multisource target data fusion tracking model to ensure the stable state of fusion results. It shows that the proposed method can save the tracking time and improve the tracking accuracy compared with the methods based on NNDA and PDA, which is more conducive to the real-time tracking of multisource targets.

Construction of Higher Education Management and Student Achievement Evaluation Mechanism Based on Apriori Algorithm

The Apriori algorithm and DM technology are introduced, examined, and a relationship between bettering teachers’ classroom teaching quality, methods, and means is discussed. An evaluation system for student achievement in higher education is proposed and built on the basis of the Apriori algorithm. The Apriori algorithm is also improved in order to further avoid blind search during mining and increase the effectiveness of frequent itemsets. This paper avoids repeatedly scanning the database and instead reads the entire database into a two-dimensional array, which increases program performance. The algorithm is used to extract useful data from the database using data association mining in the higher education management system. According to the experimental findings, the improved Apriori algorithm’s accuracy can reach 94.81%, which is 10.31% higher than the accuracy of the original Apriori algorithm. The outcomes demonstrate the applicability and reliability of the algorithm model developed in this paper. For managers in teaching and management, it can be a useful reference.

Teknik Asosiasi Datamining Dalam Menentukan Pola Penjualan dengan Metode Apriori

Camping or what is commonly called camping is an activity that is carried out outside the room. This activity usually requires some equipment such as a carriel, tent, gas cylinder, stove, mattress, salamon jacket, and several other equipment. AM Camping is a shop that sells equipment camping. AM Camping has quite a lot of sales transaction activities. Sales transaction data can be used and processed into useful information to increase sales of equipment at AM Camping stores. The problem that often occurs in AM Camping stores is that the camping equipment needed by consumers does not exist or is out of stock because the owner does not pay attention to the camping equipment stock. To solve this problem, the authors use association datamining with a priori algorithm. Based on these results, it can be obtained that there are 8 rules that meet a minimum confidence of 50%, but because of the same itemset, only one rule is chosen so that the results of the itemset rules are 5 final rules, namely if you buy a 60 L Carrier, then 94% most likely buy a Gas Cylinder, if you buy a flat stove, you are 89% most likely to buy a gas cylinder, if you buy a frem rope, then you have an 84% chance of buying a gas cylinder, if you buy a cuff, you are 73% most likely to buy a gas cylinder and if you buy a gas cylinder you are 67% likely to buy a rope frem. With this research, it is hoped that it can provide information to AM Camping shop owners in providing stock of camping equipment needed by consumers and to find out the combination of itemset from consumer purchases

Student Behavior Data Analysis Based on Association Rule Mining

With the advancement of intelligent campus data acquisition technology, student behavioral data are growing in size, variety, and real-time throughput, posing challenges to the storage capacity and computing power of traditional behavioral data analysis methods. The study focuses on the application of association rule mining in student behavioral data analysis. Data collection, storage, computation, and analysis all comprise integral parts of a four-layer data association mining architecture, and the three-step mining process from “data preprocessing” to “finding association rules” to “acquiring relevant knowledge” is described. The existing mining algorithm is updated to address the issues of overscanning of the original dataset and excess iterations. The findings from the case study reveal that the number of iterations in the modified mining algorithm is greatly lessened, effectively improving the mining efficiency of the massive student behavioral dataset.

Employment Recommendation for Education Talents Based on Big Data Precision Technology

The cornerstone of future social education is the development of skills. This work enhances the big data algorithm and presents an effective association algorithm that may boost relevance in order to improve the accuracy of educational talent employment. Furthermore, this paper employs data mining association analysis technology to identify educational talent attributes that are disproportionately important in determining the problem of student employment, as well as to eliminate attributes that are unrelated to the problem, in order to achieve the goal of screening educational talent attributes. In addition, this paper combines an improved algorithm to build an education talent employment recommendation system, builds a system function structure based on the actual situation, and combines experimental research to verify the effect of the model built in this paper. From the experimental research, it can be seen that the education talent employment recommendation system based on big data precision technology constructed in this paper can play an important auxiliary role in the education talent employment recommendation.

Evaluation and Construction of College Students’ Growth and Development Index System Based on Data Association Mining and Deep Learning Model

The rise of big data in the field of education provides an opportunity to solve college students’ growth and development. The establishment of a personalized student management mode based on big data in universities will promote the change of personalized student management from the empirical mode to the scientific mode, from passive response to active warning, from reliance on point data to holistic data, and thus improve the efficiency and quality of personalized student management. In this paper, using the latest ideas and techniques in deep learning such as self-supervised learning and multitask learning, we propose an open-source educational big data pretrained language model F-BERT based on the BERT model architecture. Based on the BERT architecture, F-BERT can effectively and automatically extract knowledge from educational big data and memorize it in the model without modifying the model structure specific to educational big data tasks so that it can be directly applied to various educational big data domain tasks downstream. The experiment demonstrates that Vanilla F-BERT outperformed the two Vanilla BERT-based models, Vanilla BERT and BERT tasks, by 0.0.6 and 0.03 percent, respectively, in terms of accuracy.

Analysis of frequent itemset generation based on trie data structure in Apriori algorithm

Apriori is one technique of data mining association rules that aims to extract correlations between sets of items in the transaction database. The main problem with the Apriori algorithm is the process of scanning databases repeatedly to generate itemset candidates. This research examines the combination of pruning by using the trieapproach and multi-thread implementation in three algorithms to obtain frequent itemset. Trie is a data structure in the form of an ordered tree to store a set of strings where every node in the tree contains the same prefix. The use of a full combination trie (different from frequent pattern (FP) tree using links) allows the implementation of arrays and the hash calculation to achieve the addressing of itemset combination. In this research, the measure to get the address is called Hash-node calculation used to update support value. For these three alternatives, run time processing is analyzed based on the number of itemset combinations and transaction data at a certain minimum support value. The experimental results show that an algorithm thatexploits resource capabilities by applying multi-threadperforms almost seven times betterthanan algorithm implemented in single-thread in calculating hash-node. The fastest run time of the multi-thread approach is 43 minutes with 150-itemset combinations on 100,000 transaction data.

FLOOD RELIEF AND CAMP MANAGEMENT SYSTEM

This is an android application used for managing after effect of flood and providing goods and services for victims Our project is about Flood Relief Camp Management System.Our intention through this project is to help the needy one, who are suffering from flood disaster.Where we mainly focused on the camp management where the volunteer can help the victim by manpower,essentials etc. Which include food,medical supplies and related stuff.Here we have a major role in controlling overflow or lack of supplies where we transfer those to the locations where its more needy.Our concept helps to balance automatically by using data mining and association mining concepts .We balance the camp essentials by comparing neighboring camps and sort it them out based on the nearest point and manage the goods by transferring.We also sort out the nearest volunteer and assign them to the nearest requested area and we track them by updating status which allow others to know whether the requested service is done.

Logistic regression model with TreeNet and association rules analysis: applications with medical datasets

This study establishes an innovative and effective approach for generating new variables and interactions for logistic regression using the two data mining techniques TreeNet and association rules analysis. With TreeNet as the first step in our logistic model building, the new variables are generated by discretizing the quantitative variables. With ASA as the following step, the new interactions are generated from all the original categorical variables and all the newly generated predictors from TreeNet. These newly generated variables and interactions (low- and high-order) are used as candidate predictors to build an optimal logistic regression model. The method is tested on and the results given for four medical datasets—heart disease, heart failure, breast cancer, and hepatitis—with the complete model process presented for the last of these. The results indicate that building a model in this way constitutes a major advance in logistic regression modeling that cannot be achieved using other existing methods.

Design and Analysis of Low Delay Deterministic Network Based on Data Mining Association Analysis

The purpose of this paper is to research on the design and analysis of low delay deterministic network based on data mining association. This paper studies and implements the algorithm of mining page association rules. A session recognition algorithm based on log reference page and request time is proposed by using the time probability relationship of continuous requests. This method improves the accuracy of log data preprocessing and page association rules mining. This paper studies and tests the efficiency, prefetch timing and cache organization of the two page association rules. The results show that in this prefetch scheme, the prefetch performance of the association rules of unordered pages is better than that of the association rules of ordered pages. The prefetch performance when cache hits is better than that when cache fails and the cache fails to hit has better performance. In the case of a certain size of cache space, reasonable organization of cache space can further improve the cache hit rate and reduce the network delay.

P2BA: A Privacy-Preserving Protocol With Batch Authentication Against Semi-Trusted RSUs in Vehicular Ad Hoc Networks

Vehicular Ad-hoc Networks (VANETs) supporting the seamless operation of autonomous vehicles introduce various network-connected devices. The widespread devices are engaged in VANETs so that users can enjoy advantageous computing and reliable services. The combination brings in massive real-time message propagation and dissemination, which would be leveraged by the adversaries to perform data association, integration analysis and privacy mining. To address such challenges, existing authentication schemes use n pseudonym certificates for pre-defined k times and try to keep the vehicles anonymous. These schemes require fresh certificates for each authentication process, which cost more communication and storage resources. In this paper, we propose a novel privacy-preserving authentication protocol (P2BA) in bilinear groups, where a registered vehicle signs a traffic-related message and sends it to the nearby Road-side Unit (RSU) together with its blinded certificate. The RSU is able to independently check the message for validity based on a non-interactive zero-knowledge proof protocol. In this way, the computation time has been reduced from <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (n) to <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1) while the storage overhead from <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (nk) to <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (n) compared to anonymous authentication protocols. Moreover, our scheme provides privacy properties such as anonymity and unlinkability. The simulations show that the message authentication can be processed by individual RSUs within 1 ms under the batch-enabled scheme, which outperforms the existing schemes in terms of computation overhead and latency.