Association Analysis Algorithm Research Articles

Data mining techniques on astronomical spectra data – III. Association analysis

ABSTRACT Association analysis is an important task that aims to investigate correlations in astronomical spectral data and mine relationships between different data features. With the rapid development of various sky survey projects, multiple association analysis methods have been applied to efficiently investigate the correlation between spectral data. However, due to the different focuses of analysing the relationships within spectral data, the performance and applicability scenarios of association analysis methods vary. We present the third article in the series to provide a comprehensive review of algorithms for astronomical spectral association analysis. First, this paper outlines the ideas and applications of association analysis algorithms for astronomical spectra in the current literature. Secondly, experiments are conducted on a unified A-type stellar spectral data set constructed based on three different signal-to-noise ratios and data volumes to examine the performance of different algorithms in analysing the correlation between data features. The results indicate that association rule algorithms can more comprehensively and effectively uncover the correlations among different spectral features, while regression analysis algorithms offer a simpler and more intuitive approach to analysing relationships between features. The spectral data used in the experiments are obtained from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope. Finally, the source code of association analysis algorithms and manuals for usage are provided on GitHub.

Out-of-set association analysis of lung cancer drugs and symptoms based on clinical case data mining.

There are 1.8 million lung cancer deaths worldwide, accounting for 18% of global cancer deaths, including 710,000 in China, accounting for 23.8% of all cancer deaths in China. To explore the out-of-set association rules of lung cancer symptoms and drugs through text mining of traditional Chinese medicine (TCM) treatment of lung cancer, and form medical case analysis to analyze the experience of TCM syndrome differentiation in its treatment. The medical records of all patients diagnosed with lung cancer in Nanjing Chest Hospital from January to December 2018 were collected, and the out-of-set association analysis was performed using the MedCase v5.2 TCM clinical scientific research auxiliary platform based on the frequent pattern growth enhanced association analysis algorithm. In terms of TCM treatment of lung cancer, the clinical symptoms with high correlation included cough, expectoration, chest distress, and white phlegm; and the drugs with high correlation included Pinellia ternata, licorice root, white Atractylodes rhizome, and Radix Ophiopogonis; with the prescriptions based on Erchen and Maimendong decoctions. This analytical study of the medical cases of TCM treatment for lung cancer was performed using data mining techniques, and the out-of-set association rules between clinical symptoms and drugs were analyzed, including the understanding of lung cancer in TCM. Moreover, the essence of experience in drug use was gathered, providing significant scientific guidance for the clinical treatment of lung cancer.

Research on the Optimization of Apriori Algorithm Based On Cloud Computing and Medical Information Data

This study proposes an Hadoop-based improved Apriori algorithm (IM Apriori) that optimizes the efficiency of data processing by reducing the set of candidate items and the time to calculate support. The improved algorithm solves two major bottlenecks in traditional Apriori algorithms: scanning databases frequently and generating a large number of candidate sets. Specific improvements include adopting new database mapping methods, avoiding repeatedly scanning the database, further pruning frequent and candidate sets, and calculating support using overlapping strategies to achieve efficient data processing. To verify the performance of the improved algorithm, simulation experiments were conducted on the Hadoop platform and compared with the traditional Apriori algorithm, BITXOR algorithm and several other association analysis algorithms. The experimental results show that the improved IM Apriori algorithm is better than the traditional and other improved algorithms in terms of efficiency and performance, which significantly reduces the number of database scans and alleviates the input / output bottleneck problem. Through this study, we prove the application potential of cloud computing based and improved data mining algorithm in medical data analysis, and improve the efficiency and accuracy of medical data mining.

Algorithm Optimization and Performance Improvement of Data Visualization Analysis Platform based on Artificial Intelligence

With the rapid development of artificial intelligence, data visualization analysis platforms have been widely applied in various fields. This study mainly explores the optimization and performance improvement of algorithms for data visualization analysis platforms based on artificial intelligence. Firstly, the definition of a data visualization analysis platform and the application of artificial intelligence in it were introduced, and the current problems and challenges were pointed out. Then, a discussion was conducted on algorithm optimization for various stages of research, including data preprocessing, data clustering, data classification, and optimization of data association analysis algorithms. Subsequently, the study proposed some performance improvement methods, including the application of parallel computing technology, distributed computing technology, data compression technology, and data indexing technology.

Cybersecurity knowledge graph enabled attack chain detection for cyber-physical systems

The Cyber-Physical System covers a wide range of applications, many of which are involved in critical infrastructure, and the cybersecurity attacks on them become more and more threatening. Currently, most of the comprehensive analysis of compound attacks depend on the experience of security analysts. To improve the efficiency and accuracy of compound attack research, this paper introduces a knowledge graph into compound attack detection and constructs a cybersecurity knowledge graph based on the knowledge of known attacks. The cybersecurity knowledge graph can carry out correlation analysis on real-time data to restore the attack process. The main work of this paper is to construct the cybersecurity knowledge graph and to apply mining found compound attacks automatically. Besides, a multi-dimensional data association analysis algorithm based on dynamic clustering mechanism, and an attack chain complementation-pruning method based on optimal reaching path queries are proposed to solve the problem of low efficiency in correlation analysis caused by redundant data and the problem of missing and misunderstandings in the collection data. Experiments show that the cyber security knowledge graph construction method and attack chain optimization-pruning method proposed in this paper improve the accuracy and efficiency of attack chain mining.

Research on Mathematical Modeling Competition and Ideological and Political Education Based on Analysis and Prediction Algorithm

This paper selects the research of mathematical modeling competition and correlation analysis of ideological and political education based on analysis and prediction algorithm, including corresponding analysis algorithm, time series prediction algorithm, bp neural network prediction algorithm, and grey association analysis algorithm. First of all, when the corresponding analysis method is used, based on the extraction of a large number of ideological and political documents, the indicators of the past three years were obtained, and the four indicators of "teacher system, reform and innovation, cultural integration, and student classroom" were clustered to obtain the status quo and development trend of the four indicators. For the prediction of the future ideological and political education, we used the time-series algorithm and the BP neural network algorithm to derive the number of published articles and the development trends in the ideological and political field in the next two years.Using grey correlation analysis, with "mathematical modeling, politics" as the theme, to all relevant literature retrieval, extract representative 100 articles, to all the factors involved in the article clustering, red elements, traditional culture, classroom atmosphere, personal value, national situation five related factors, analyze the influence of grey correlation, get the results from high to low order: red elements 0.611,0.596, traditional culture, classroom atmosphere 0.594, personal value of 0.593, national situation 0.581, are strong influence factors, so as to prove the rationality of the model.

Sports Injury Risk Prevention and MRI Image Performance of Athletes in Physical Education.

In order to effectively prevent athletes' injury during sports training in physical education, a method of risk prevention of sports injury based on MRI technology was proposed. This method solves the problem of injury prevention in sports training by studying the association analysis algorithm in data mining technology and the research of MRI technology. The experimental results show that the average prediction error of CT and US is about 5%, so it can be considered that the model can predict accurately. Conclusion. The method of risk prevention of sports injury based on MRI technology can effectively prevent the injury of athletes in the process of sports training and reduce the injury rate of athletes.

Sports Medical Image Modeling of Injury Prevention in Dance Learning and Sports Training.

In order to effectively prevent injuries in dance learning and sports training, this paper proposes a method based on sports medical image modeling. This method solves the problem of injury prevention in dance learning by studying the association analysis algorithm, medical image information system, and CT technology and analyzing the role of data mining technology in the medical image information system. The experimental results show that the average prediction error of CT and US is about 5%, which can be considered that the model can predict accurately. The error of MR is as high as 28.2%, and the prediction is relatively inaccurate. Conclusion. the model can effectively prevent the injury in training.

Sports Economic Mining Algorithm Based on Association Analysis and Big Data Model.

With the implementation of national strategies such as sports power and national fitness, the sports economy has become an important element of high-quality national development, and the demand for sports economy and management talents is greatly increased. Particularly in the new area with big data as the typical feature, the teaching content, teaching method, and teaching mode of sports economics and management majors have put forward new requirements. The continuous progress of storage and network technology has prompted the generation of massive multisource spatiotemporal data in various fields. The advantage of association analysis algorithms is that they are easy to code and implement. The relationships found by association analysis can take two forms: frequent itemsets or association rules. We use correlation analysis methods to perform correlation learning between sports economy and related big data and thus improve the development of sports economy. Mining and analyzing the relevant big data can precisely reveal the problems of sports economic development and can realize the fine management of sports, thus contributing to the healthy development of sports. Mastering the skills of acquiring, analyzing, and applying big data is the core content of sports economic analysis. The sports economy has refined and intelligent management means, and its adoption of virtual reality reflects the current situation and development trend of the sports business, which further highlights the status and role of multisource big data in the sports economy. Based on these, this paper proposed a sports economy mining algorithm in view of the correlation analysis and big data model. Then, we verified the effectiveness of the model through experiments, which laid the foundation for the development of the sports economy.

A proposed strategy to secure web usage

With much data on the web, it can be difficult, frustrating, and seemingly impossible to find the exact information you need. There are many powerful search utilities on the web are called search engines, in addition to the visitor tracking in a web to study exactly the behavior of the web visitors, to improve the efficiency of that web. This research concentrates on a particular aspect, which is applying Data mining technique especially by association analysis algorithm on the encrypted web log files, that to ensure the privacy of the original data for these files. So since the input data introduced to mining algorithm is encrypted then the resulted association rules are encrypted that to ensure the privacy of the extracted knowledge. Then analyze the decrypted web log data file for the web usage, to study the visitor tracking. According to this study the server configurations and all the services will be improved.

Defect Data Association Analysis of the Secondary System Based on AFWA-H-Mine

The fault data of the secondary system of smart substations hide some information that the association analysis algorithm can mine. The convergence speed of the Apriori algorithm and FP-growth algorithm is slow, and there is a lack of indicators to evaluate the correlation of association rules and the method to determine the parameter threshold. In this paper, the H-mine algorithm is used to realize the fast mining of fault data. The algorithm can traverse data faster by using the data structure of the H-struct. This paper also sets the lift and CF value to screen the association rules with good correlation. When setting the three key parameters of association analysis, namely, support threshold, confidence threshold, and lift threshold, an objective function composed of weighted average lift, CF value, and data coverage rate was selected, and the adaptive fireworks algorithm was used to optimize the parameters in the association analysis. In particular, the rule screening strategy is introduced in fault cause analysis in this paper. By eliminating rules with high similarity, derived signals in association rules are eliminated to the greatest extent to improve the readability of rules and ensure easy understanding of results.

Analysis of TCM Diagnosis and Treatment of Thyroid Diseases Based on Data Mining

Thyroid disease is the most common and frequently occurring endocrine disease. In recent years, the incidence rate of thyroid diseases has increased in recent years due to the gradual change of the ecological environment and the gradual popularization of iodine supplements for iodine deficiency disorders. At present, the prevalence of hyperthyroidism is 1.3%, hypothyroidism is 6.5%, thyroid nodules are 18.6%, and 2%-5% of thyroid nodules are thyroid cancer. Thyroid disease has become a high incidence and common disease in modern society. The number of thyroid disease patients in China has become the second largest disease after hypertension. Thyroid disease belongs to gall disease of traditional Chinese medicine. Compared with western medicine, traditional Chinese medicine has certain advantages in the treatment of gall disease. Compared with western medicine, traditional Chinese medicine can better improve clinical symptoms and reduce toxicity.In this paper, Stata and python were used to analyze and clean the data of TCM diagnosis thyroid medical records. Principal component analysis and factor analysis were used to analyze and clean the symptoms and pathogenesis of the medical record data. The data in the prescription were quantified. The feature selection of the data set was performed by principal component analysis to reduce the data dimension. In the process of treatment, different kinds of prescriptions will be used. Association analysis algorithm in data mining is used to mine the association rules of prescription medication and complications of thyroid disease, which provides a reference for clinical treatment drug selection and disease prevention of traditional Chinese medicine. Logistic regression was used to establish the prediction model of traditional Chinese medicine diagnosis prescription of thyroid by using the clinical data of traditional Chinese medicine.

Security monitoring of heterogeneous networks for big data based on distributed association algorithm

With the continuous development of heterogeneous networks, the structure and scale of the network are becoming more and more complex, and the difficulty of processing network alarm information is also increasing. Therefore, an efficient and flexible alarm handling scheme will become especially important in the alarm management of heterogeneous networks. In the aspect of alarm analysis of heterogeneous networks, this paper proposes a corresponding distributed alarm analysis model and corresponding distributed association analysis algorithm. Combined with the structural characteristics of heterogeneous networks, the proposed distributed analysis model has many potential advantages. One of them is the significant reduction in the candidate set in the alarm database, which can greatly help us improve the efficiency of alarm correlation analysis in heterogeneous networks, thereby further reducing the time required for our alarm correlation analysis. In many respects, such improvement provides good technical support for the management of alarm information in heterogeneous networks. Finally, the simulation verification of the corresponding algorithm is given. The results show that under different support thresholds, the distributed association analysis algorithm has shorter running time.

Big Data Platform Access Control Rule Generation Method Based on Data Mining

The access control problem of big data platform is related to personal privacy, corporate interests and national security. In the context of big data platform, the fine-grained permission relationship between users and resources is difficult to be explored through the experience of administrators, and the permission granularity is difficult to be refined. In this paper, we propose an access control rule generation method based on data mining. By selecting appropriate data preprocessing, clustering analysis, association analysis algorithms and improving them, we dig out the normal access behavior rules of users from the user logs and attributes, and generate fine-grained control rules based on these rules, and improve the accuracy through negative feedback regulation. The experiment results verify the effectiveness and practicability of the method which can provide accurate access control for the big data platform.

AN IMPROVED AND EFFICIENT METHOD TO DISCOVER THE FREQUENT PATTERNS FROM TARGETED PATTERNS IN TRANSACTIONAL DATASET USING TPIITR-FPMM

In Recent years, Data mining is an essential technique to discover useful knowledge from transactional dataset. Association analysis algorithm is one of the vital data mining techniques. It normally catches relationships among items in transactional dataset. Generally they are used to develop the strategy of the future business. The main step of association analysis is to catch the frequent patterns in large transactional dataset. Plenty of methods are available in the literature to catch the frequent patterns. Most of the techniques gave in the literature catch all frequent itemsets for a specified minimum support threshold value. But in some instance, it is desired to examine the existence of some of the few targeted patterns (for example special offer given for group of items to promote the retail sales) in large transactional dataset to develop the strategy of the future business. For this purpose, we previously introduced SIFPMM (Selective Itemsets Frequent Pattern Mining Method) method and TM-PIFPMM (Transaction Merging-Predefined Itemsets Frequent Pattern Mining Method). To improve the performance of TM-PIFPMM, this TPIITR-FPMM (Targeted Patterns Involved Items Transaction Reduction-Frequent Pattern Mining Method) is proposed and the performance of this method is compared with Apriori, FP-Growth, SIFPMM and TM-PIFPMM. The Experimental analysis of TPIITR-FPMM verifies that this method outperforms than Apriori, FP-Growth, SIFPMM and TM-PIFPMM.

Situation Awareness in Ambient Assisted Living for Smart Healthcare

The success of providing smart healthcare services in ambient assisted living (AAL) largely depends on an effective prediction of situations in the environment. Situation awareness in AAL is to determine the environment smartness by perceiving information related to the surroundings and human behavioral changes. In AAL environment, there are plenty of ways to collect data about its inhabitants, such as through cameras, microphones, and other sensors. The collected data are complicated enough to go for an efficient processing in perceiving the situation. This paper gives an overview of the existing research results in multimodal data analysis in AAL environment to improve the living environment of the seniors, and it attempts to bring efficiency in complex event processing for real-time situational awareness. This paper thus considers multimodal sensing for detection of current situations as well as to predict future situations using decision-tree and association analysis algorithms. To illustrate the proposed approach, we consider elderly activity recognition in the AAL environment.

Research on Efficient Association Analysis Algorithm towards Production Process Data

Association analysis of the production process data (PPD) can be discoveried on the quality relevant parameters with great impact, however, it’s different from correlation analysis of other fields, Huge amount of data due to the production process, and the many parameters involved in the production process, the existing association analysis algorithms as they deal with inefficiency, can not meet the practical application. This paper proposes a new process for the industrial production of efficient data association algorithm-AprioriMask, after the actual production process of association analysis verification, AprioriMask algorithm has significant performance improvements to meet the industrial production process data for correlation analysis.

Lessons Learned from Data Mining of WHO Mortality Database

The objectives of this research were to test the ability of classification algorithms to predict the cause of death in the mortality data with unknown causes, to find association between common causes of death, to identify groups of countries based on their common causes of death, and to extract knowledge gained from data mining of the World Health Organization mortality database. The WEKA software version 3.5.3 was used for classification, clustering and association analysis of the World Health Organization mortality database which contained 1,109,537 records. Three major steps were performed: Step 1 - preprocessing of data to convert all records into suitable formats for each type of analysis algorithm; Step 2 - analyzing data using the C4.5 decision tree and Naïve Bayes classification algorithm, K-means clustering algorithm and Apriori association analysis algorithm; Step 3 - interpretation of results and hypothesis testing after clustering analysis. Using a C4.5 decision tree classifier to predict cause of death, we obtained 440 leaf nodes that correctly classify death instances with an accuracy of 40.06%. Naïve Bayes classification algorithm calculated probability of death from each disease that correctly classify death instances with an accuracy of 28.13%. K means clustering divided the data into four clusters with 189, 59, 65, 144 country-years in each cluster. A Chi-square was used to test discriminate disease differences found in each cluster which had different diseases as predominant causes of death. Apriori association analysis produced association rules of linkage among cancer of the lung, hypertension and cerebrovascular diseases. These were found in the top five leading causes of death with 99-100% confidence level. Classification tools produced the poorest results in predicting cause of death. Given the inadequacy of variables in the WHO database, creation of a classification model to predict specific cause of death was impossible. Clustering and association tools yielded interesting results that could be used to identify new areas of interest in mortality data analysis. This can be used in data mining analysis to help solve some quality problems in mortality data.

A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules

There is a challenging man–machine-interface issue in existing association analysis algorithms because they are Apriori-like and the Apriori Algorithm is based on the assumption that users can specify the threshold: minimum-support. It is impossible that users give a suitable minimum-support for a database to be mined if the users are without knowledge concerning the database. In this paper, we propose a fuzzy mining strategy with database-independent minimum-support, which provides a good man–machine interface that allows users to specify the minimum-support threshold without any knowledge concerning their databases to be mined. We have evaluated the proposed approach and the experimental results have demonstrated that our algorithm is promising and efficient.