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P.Raja Rajeswari, Dr, P. Surya Teja, P. Sri Harsha, and T. Chaitanya Kumar. "Enhancing the Performance of Crime Prediction Technique Using Data Mining." International Journal of Engineering & Technology 7, no. 2.32 (2018): 424. http://dx.doi.org/10.14419/ijet.v7i2.32.15731.
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Time is a important factor for criminal sentencing. Most of the times, criminal released on the bail which may harmful to the society, even after they have furnished the judgment. This sort of threats cut down through the prediction analysis. The analysis can be done on the concerned person to analyse that she/ he is about to do the crime. So that, its benefit not only for law enforcement but also for country safety. Data Mining, a method which handles a massive datasets. Data mining also used to guess desired patterns. Police Officers are the best persons for crime prediction and also predict the criminal’s upcoming activities. We are here implementing the use of Frequent Mining Pattern in addition to Association Rule Mining. The main aim of this paper is analysing several crimes by various criminals and to predict chance of crimes by same criminal. It will be helpful for Country Law Enforcement and safeguarding from criminals who were released on Bail. Aim can be achieved with the help of Apriori Algorithm.
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Wu, You, Zheng Wang, and Shengqi Wang. "Human Resource Allocation Based on Fuzzy Data Mining Algorithm." Complexity 2021 (June 10, 2021): 1–11. http://dx.doi.org/10.1155/2021/9489114.
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Data mining is currently a frontier research topic in the field of information and database technology. It is recognized as one of the most promising key technologies. Data mining involves multiple technologies, such as mathematical statistics, fuzzy theory, neural networks, and artificial intelligence, with relatively high technical content. The realization is also difficult. In this article, we have studied the basic concepts, processes, and algorithms of association rule mining technology. Aiming at large-scale database applications, in order to improve the efficiency of data mining, we proposed an incremental association rule mining algorithm based on clustering, that is, using fast clustering. First, the feasibility of realizing performance appraisal data mining is studied; then, the business process needed to realize the information system is analyzed, the business process-related links and the corresponding data input interface are designed, and then the data process to realize the data processing is designed, including data foundation and database model. Aiming at the high efficiency of large-scale database mining, database development tools are used to implement the specific system settings and program design of this algorithm. Incorporated into the human resource management system of colleges and universities, they carried out successful association broadcasting, realized visualization, and finally discovered valuable information.
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Saltos, Ginger, and Mihaela Cocea. "An Exploration of Crime Prediction Using Data Mining on Open Data." International Journal of Information Technology & Decision Making 16, no. 05 (2017): 1155–81. http://dx.doi.org/10.1142/s0219622017500250.
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The increase in crime data recording coupled with data analytics resulted in the growth of research approaches aimed at extracting knowledge from crime records to better understand criminal behavior and ultimately prevent future crimes. While many of these approaches make use of clustering and association rule mining techniques, there are fewer approaches focusing on predictive models of crime. In this paper, we explore models for predicting the frequency of several types of crimes by LSOA code (Lower Layer Super Output Areas — an administrative system of areas used by the UK police) and the frequency of anti-social behavior crimes. Three algorithms are used from different categories of approaches: instance-based learning, regression and decision trees. The data are from the UK police and contain over 600,000 records before preprocessing. The results, looking at predictive performance as well as processing time, indicate that decision trees (M5P algorithm) can be used to reliably predict crime frequency in general as well as anti-social behavior frequency.
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Sekhar Babu, B., P. Lakshmi Prasanna, and P. Vidyullatha. "Personalized web search on e-commerce using ontology based association mining." International Journal of Engineering & Technology 7, no. 1.1 (2017): 286. http://dx.doi.org/10.14419/ijet.v7i1.1.9487.
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In current days, World Wide Web has grown into a familiar medium to investigate the new information, Business trends, trading strategies so on. Several organizations and companies are also contracting the web in order to present their products or services across the world. E-commerce is a kind of business or saleable transaction that comprises the transfer of statistics across the web or internet. In this situation huge amount of data is obtained and dumped into the web services. This data overhead tends to arise difficulties in determining the accurate and valuable information, hence the web data mining is used as a tool to determine and mine the knowledge from the web. Web data mining technology can be applied by the E-commerce organizations to offer personalized E-commerce solutions and better meet the desires of customers. By using data mining algorithm such as ontology based association rule mining using apriori algorithms extracts the various useful information from the large data sets .We are implementing the above data mining technique in JAVA and data sets are dynamically generated while transaction is processing and extracting various patterns.
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Nithya, NS, and K. Duraiswamy. "Correlated gain ratio based fuzzy weighted association rule mining classifier for diagnosis health care data." Journal of Intelligent & Fuzzy Systems 29, no. 4 (2015): 1453–64. http://dx.doi.org/10.3233/ifs-151614.
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Yoseph, Fahed, and Markku Heikkilä. "A new approach for association rules mining using computational and artificial intelligence." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7233–46. http://dx.doi.org/10.3233/jifs-200707.
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Market Intelligence is knowledge extracted from numerous data sources, both internal and external, to provide a holistic view of the market and to support decision-making. Association Rules Mining provides powerful data mining techniques for identifying associations and co-occurrences in large databases. Market Basket Analysis (MBA) uses ARM to gain insights from heterogeneous consumer shopping patterns and examines the effects of marketing initiatives. As Artificial Intelligence (AI) more and more finds its way to marketing, it entails fundamental changes in the skills-set required by marketers. For MBA, AI provides important ways to improve both the outcomes of the market basket analysis and the performance of the analysis process. In this study we demonstrate the effects of AI on MBA by our proposed new MBA model where results of computational intelligence are used in data preprocessing, in market segmentation and in finding market trends. We show with point-of-sale (POS) data of a small, local retailer that our proposed “Åbo algorithm” MBA model increases mining performance/intelligence and extract important marketing insights to assess both demand dynamics and product popularity trends. Additionally, the results show how, as related to the 80/20 percent rule, 78% of revenue is derived 16% of the product assortment.
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Tseng, Ming-Hseng, and Hui-Ching Wu. "Investigating Health Equity and Healthcare Needs among Immigrant Women Using the Association Rule Mining Method." Healthcare 9, no. 2 (2021): 195. http://dx.doi.org/10.3390/healthcare9020195.
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Equitable access to healthcare services is a major concern among immigrant women. Thus, this study investigated the relationship between socioeconomic characteristics and healthcare needs among immigrant women in Taiwan. The secondary data was obtained from “Survey of Foreign and Chinese Spouses’ Living Requirements, 2008”, which was administered to 5848 immigrant women by the Ministry of the Interior, Taiwan. Additionally, descriptive statistics and significance tests were used to analyze the data, after which the association rule mining algorithm was applied to determine the relationship between socioeconomic characteristics and healthcare needs. According to the findings, the top three healthcare needs were providing medical allowances (52.53%), child health checkups (16.74%), and parental knowledge and pre- and post-natal guidance (8.31%). Based on the association analysis, the main barrier to the women’s healthcare needs was “financial pressure”. This study also found that nationality, socioeconomic status, and duration of residence were associated with such needs, while health inequality among aged immigrant women was due to economic and physical factors. Finally, the association analysis found that the women’s healthcare problems included economic, socio-cultural, and gender weakness, while “economic inequality” and “women’s health” were interrelated.
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Cheng, Haodong, Meng Han, Ni Zhang, Xiaojuan Li, and Le Wang. "A Survey of incremental high-utility pattern mining based on storage structure." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 841–66. http://dx.doi.org/10.3233/jifs-202745.
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Traditional association rule mining has been widely studied, but this is not applicable to practical applications that must consider factors such as the unit profit of the item and the purchase quantity. High-utility itemset mining (HUIM) aims to find high-utility patterns by considering the number of items purchased and the unit profit. However, most high-utility itemset mining algorithms are designed for static databases. In real-world applications (such as market analysis and business decisions), databases are usually updated by inserting new data dynamically. Some researchers have proposed algorithms for finding high-utility itemsets in dynamically updated databases. Different from the batch processing algorithms that always process the databases from scratch, the incremental HUIM algorithms update and output high-utility itemsets in an incremental manner, thereby reducing the cost of finding high-utility itemsets. This paper provides the latest research on incremental high-utility itemset mining algorithms, including methods of storing itemsets and utilities based on tree, list, array and hash set storage structures. It also points out several important derivative algorithms and research challenges for incremental high-utility itemset mining.
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Wang, Liwen, and Soo-Jin Chung. "Sustainable Development of College and University Education by use of Data Mining Methods." International Journal of Emerging Technologies in Learning (iJET) 16, no. 05 (2021): 102. http://dx.doi.org/10.3991/ijet.v16i05.20303.
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To improve the education efficiency of the students, the student-centered education plan is explored. First, the Apriori algorithm of association rules is used to mine the potential related patterns in the score data of college students and establish a reasonable teaching method. Second, aided by the decision tree model, the factors affecting students' academic performance are studied, and the potential relationship between different courses is studied. Finally, the Apriori algorithm of association rules combined with decision tree model is used to generate the early warning mechanism of students' achievement, and the course performance of college students is empirically analyzed. The results show that: C language has two sides of dependence on many subjects; higher mathematics → linear algebra → mathematical statistics → computer composition principle → computer network. The teaching scheme of C language → C + + → Java more conforms to the learning mechanism of college students. Through empirical analysis, the early warning mechanism of association rule Apriori algorithm and decision tree model can effectively analyze student's course and give student's achievement. It is found that the method proposed can provide theoretical basis for students, teachers, and university administrators to carry out education reform and education management decision-making, improve students' performance and education quality, and realize the "student-oriented" education concept, so it can be applied to the actual education management.
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Senthil, D., and G. Suseendran. "Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine." International Journal of Engineering & Technology 7, no. 3.3 (2018): 218. http://dx.doi.org/10.14419/ijet.v7i2.33.13890.
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Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.
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Mobo, Froilan D. "Using Data Mining In Learning Management Systems Amidst Covid-19." Aksara: Jurnal Ilmu Pendidikan Nonformal 6, no. 3 (2020): 213. http://dx.doi.org/10.37905/aksara.6.3.213-216.2020.
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<p>The Second Semester of Academic Year 2019-2020 was temporarily suspended due to the widespread COVID-19 last March 16, 2020, forcing the President of the Republic of the Philippines, Hon. Rodrigo Roa Duterte imposed an Enhanced Community Quarantine in Luzon which is known as a lockdown closing all the border points of each town and provinces. One of the major problem encountered during the lockdown is the suspension of classes because as per IATF guidelines you need to stay home, the said Memorandum Order was posted in the official gazette, (Medialdea, 2020)</p><p>The dataset on the features of the Learning Management Systems using Moodle is that Professors will be the one who will set the topics, quizzes, and exercises for his class even the assessment methods on the system. To prevent from slowing down the network, the Team of Seaversity the developer of the learning management systems headed by C/E Ephrem Dela Cernan conducts a ZOOM Training to all Faculty to be familiarized more on the Learning Management Systems of the Philippine Merchant Marine Academy. </p><p>The Moodle Learning Management Systems is a user-friendly environment because of its features and users can easily adjust from the traditional face to face teaching going to e-Learning approach because of it’s all capabilities as a data mining methods such as statistics, association rule mining, pattern mining visualization, categorization, clustering, and text mining., (AlAjmi &amp; Shakir, 2013)</p>
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Ma, Yu Ling. "A Recommendation System of Highway ETC Card Based on Decision Tree Theory." Applied Mechanics and Materials 644-650 (September 2014): 2411–15. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2411.
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With the promotion of social information construction and the rapid update and replacement of large capacity storage equipment, the amount of data from every field grows exponentially. Reportedly, the amount of the data accumulated by Shandong Hi-speed Group is very large. These data can satisfy us some daily usefulness, such as query, retrieval, statistics, statements etc. But what is more important is that how can we discover some useful information from the information ocean. This information can be used in real life such as auxiliary decision. This paper is proposed in this historical background. Data mining is a powerful tool for acquiring knowledge from massive data. Some methods of data mining, such as decision tree, support vector machine, Bayesian decision theory, artificial neural network, k-nearest neighbor, association rule mining etc, are commonly used. In this paper, we design a recommendation system of highway ETC card by using the theory of decision tree. The recommendation system can predict whether a car owner is a potential ETC customer or not through the analysis of the vehicle information. Experiments proved that the accuracy rate of the recommendation system is larger than 90%, so it can provide effective information for the extension of China's ETC card.
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Ramzan Begam, M., and P. Sengottuvelan. "Crime Case Reasoning Based Knowledge Discovery Using Sentence Case Relative Clustering for Crime Analyses." International Journal of Engineering & Technology 7, no. 3.27 (2018): 91. http://dx.doi.org/10.14419/ijet.v7i3.27.17662.
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Day to day involvement in crime becomes higher statistics for providing information against crime occurrences. A crime committed in different locations, the point of crime occurrence, strategy be analyzed very tedious using only information records. Because information collection in the form of attribute case records with direct crime rates score, so valid factor identification of crime category is a problem. By using the crime cluster in data mining technique to analyze the criminal records to propose a sentence case relative clustering algorithm (SCRCA)with addition classification rule mining algorithm to solve the crime problems. Also to use the sentence case observer technique for knowledge discovery from the crime records for proper case identification from sentence case records and to help increase the predictive accuracy. Crime examines a developing method and identifying the field in law implementation without standard definitions for correct judgment. With the expanding utilization of the clustering automated frameworks to track crimes, information examiners helping the law implementation officers and analysts to accelerate the way toward measuring crimes. The main contribution is to analyses the attribute case with relative sentences of count word analyzes factor for improving the crime prediction for categorizing crime type.
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Fadel, Cristina Berger, Danielle Bordin, Celso Bilynkievycz dos Santos, Deborah Ribeiro Carvalho, and Suzely Adas Saliba Moimaz. "Users’ satisfaction with the public dental service: the discovery of new patterns." Cadernos Saúde Coletiva 27, no. 2 (2019): 172–81. http://dx.doi.org/10.1590/1414-462x201900020008.
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Abstract Background Regarding to oral health, little has been advanced on how to improve quality within dental care. Objective The aim of this study was to identify the demographic factors affecting the satisfaction of users of the dental public service having the value of a strategic and high consistency methodology. Method The Data Mining was used to a secondary database, contemplating 91 features, segmental in 9 demographic factors, 17 facets, and 5 dominions. Descriptive statistics were extracted to a demographic data and the satisfaction of the users by facets and dominions, being discovered as from Decision Trees and Association Rules. Results the analysis of the results showed the relation between the demographic factor 'professional occupation' and satisfaction, in all of the dominions. The occupations of general assistant and home assistant with daily wage stood out in Association Rules to represent the lower level of satisfaction compared to the facets that were worse evaluated. Also, the factor ‘health unit's name’ showed relation with most of the investigated dominions. The difference between health units was even more evident through the Association Rule. Conclusion The Data Mining allowed to identify complementary relations to the user's perception about the public oral health services quality, constituting a safe tool to support the management of Brazilian public health and the basis of future plans.
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Luo, Changyong, He Yu, Tao Yang, et al. "Data Mining and Systematic Pharmacology to Reveal the Mechanisms of Traditional Chinese Medicine in Recurrent Respiratory Tract Infections’ Treatment." Evidence-Based Complementary and Alternative Medicine 2020 (October 26, 2020): 1–13. http://dx.doi.org/10.1155/2020/8979713.
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Traditional Chinese medicine (TCM) was widely used in the treatment of recurrent respiratory tract infections (RRTIs) in East Asia, but its mechanism was not clear because of its complex prescription rules. This research prospectively collected 100 prescriptions of RRTI children treated with TCM. The characteristics of TCM in prescriptions were described and analyzed, and the rules of prescriptions were analyzed by hierarchical clustering and association rules. The results showed that the principle of RRTI was to pay equal attention to cold and mild, and six new meaningful prescriptions were obtained. Among them, the new prescription composed of Astragali Radix (Huangqi), Atractylodis Macrocephalae Rhizoma (Baizhu), Saposhnikoviae Radix (Fangfeng), Angelicae Sinensis Radix (Danggui), and Paeoniae Radix Rubra (Chishao) was an important method to treat RRTI. In order to explore the mechanism of the new prescription, the research obtained the action target of each herb of the core prescription on Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine, TCMIP v2.0. The target genes were enriched by Metascape, and 93 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were obtained. According to the classification and statistics of KEGG type, it was found that the new prescription mainly intervened in the metabolic pathway dominated by amino acid metabolism. In addition, there were also many interventions in the nervous system-, endocrine system-, and digestive system-related pathways. This study summarized the prescription rule of TCM in the treatment of RRTI, analyzed the mechanism of supplementing deficiency, and provided a new idea for the treatment of RRTI.
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Zheng, Hui, Peng LI, and Jing HE. "A Novel Association Rule Mining Method for Streaming Temporal Data." Annals of Data Science, June 7, 2021. http://dx.doi.org/10.1007/s40745-021-00345-w.
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Zhang, Hongrui. "Application of Association Rule Data Mining in Statistical Analysis of College Students' Mental Health." CONVERTER, July 28, 2021, 716–24. http://dx.doi.org/10.17762/converter.250.
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Strengthening the application of big data technology in data analysis can effectively improve the service capability and level of relevant statistics, and provide comprehensive and reliable information support for macro decision-making and trend analysis. This paper comprehensively reviews the research status of big data technology in the field of college students' mental health at home and abroad. Combining with the characteristics of college students' mental health statistical data and the weaknesses in statistical analysis, the feasibility of using knowledge mapping technology is demonstrated. On this basis, the blood relationship graph and influence analysis among the statistical indicators of college students' mental health were constructed through the knowledge map. The application of the knowledge map of college students' mental health statistical indicators in statistical data analysis, statistical indicator identification and statistical data quality management is proposed. Specifically, based on the concept of big data, we can establish a decision analysis platform for college students' mental health. Based on the big data technology, the data mining and analysis ability can be enhanced. In addition, it can change the traditional thinking of college students' mental health statistics and strengthen the construction of statistical team.
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Ceddia, Gaia, Liuba Nausicaa Martino, Alice Parodi, Piercesare Secchi, Stefano Campaner, and Marco Masseroli. "Association rule mining to identify transcription factor interactions in genomic regions." Bioinformatics, September 3, 2019. http://dx.doi.org/10.1093/bioinformatics/btz687.
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Abstract Motivation Genome regulatory networks have different layers and ways to modulate cellular processes, such as cell differentiation, proliferation, and adaptation to external stimuli. Transcription factors and other chromatin-associated proteins act as combinatorial protein complexes that control gene transcription. Thus, identifying functional interaction networks among these proteins is a fundamental task to understand the genome regulation framework. Results We developed a novel approach to infer interactions among transcription factors in user-selected genomic regions, by combining the computation of association rules and of a novel Importance Index on ChIP-seq datasets. The hallmark of our method is the definition of the Importance Index, which provides a relevance measure of the interaction among transcription factors found associated in the computed rules. Examples on synthetic data explain the index use and potential. A straightforward pre-processing pipeline enables the easy extraction of input data for our approach from any set of ChIP-seq experiments. Applications on ENCODE ChIP-seq data prove that our approach can reliably detect interactions between transcription factors, including known interactions that validate our approach. Availability and implementation A R/Bioconductor package implementing our association rules and Importance Index-based method is available at http://bioconductor.org/packages/release/bioc/html/TFARM.html. Contact gaia.ceddia@polimi.it Supplementary information Supplementary data are available at Bioinformatics online.
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Mahmmud, Ayman Altaher, and W. Jebersen. "A Comparitve Study of Anlyzing Data Minig Methods in Libyan National Crime Data." Computing Trendz - The Journal of Emerging Trends in Information Technology 6, no. 1 (2016). http://dx.doi.org/10.21844/cttjetit.v6i1.6689.
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Law enforcement agencies which are represented in the police today are faced with large volume of data that must be processed and transformed into useful information and hence data mining can greatly improve crime analysis and aid in reducing and preventing crime as much as possible. Crime reports and data are used as an input for the formulation of the crime prevention policies and strategic plans. This work will apply some data mining methods to analyses Libyan national criminal record data to help the Libyan government to make a strategically decision regarding prevention the increasing of the high crime rate these days. The data was collected manually from Benghazi, Tripoli, and Al-Jafara Supremes Security Committee (SSC). A comparative study will be conducted with a recent model used in the Federal Bureau of Investigation (FBI) to detect and classify the major personality and behavioral characteristics of an individual based upon analysis of the crime or crimes the person committed. Our proposed model will be able to extract crime patterns by using association rule mining and clustering to classify crime records on the basis of the values of crime attributes.
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Fan, Ching-Lung. "Data mining model for predicting the quality level and classification of construction projects." Journal of Intelligent & Fuzzy Systems, July 2, 2021, 1–15. http://dx.doi.org/10.3233/jifs-219182.
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Project managers supervise projects to ensure their smooth completion within a stipulated time frame and budget while guaranteeing construction quality. The relationships of various attributes with quality can be quantified and classified to facilitate such supervision. Therefore, this study used a data mining algorithm to analyze the relationships between defects, quality levels, contract sums, project categories, and progress in 1,015 inspection projects. In the first part, association rule mining (ARM), an unsupervised data mining approach, was used to obtain 11 rules relating two defect types (i.e., quality management system and construction quality) and determine the relationships between the four attributes (i.e., quality level, contract sum, project category, and progress). The resulting association rule may be beneficial for construction management because project managers can use it to determine the correlations between defects and attributes. In the second part, supervised data mining techniques, namely neural network (NN), support vector machine (SVM), and decision tree (C5.0 and QUEST) algorithms, were applied to develop a classification model for quality prediction. The target variable was quality, which was divided into four levels, and the decision variables comprised 499 defects, 3 contract sums, 7 project categories, and 2 progress variables. The results indicated that five defects were important. Finally, the four indicators of gain chart, break-even point (BEP), accuracy, and area under the curve (AUC) were calculated to evaluate the model. For the SVM model, the actual value predicted by the gain chart was 96.04%, the BEP was 0.95, and the AUC was 0.935. The SVM yielded optimal classification efficiency and effectively predicted the quality level. The data mining model developed in this study can serve as a reference for effective construction management.
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Liu, Maidi, Yanqing Ye, Jiang Jiang, and Kewei Yang. "MANIEA: a microbial association network inference method based on improved Eclat association rule mining algorithm." Bioinformatics, May 10, 2021. http://dx.doi.org/10.1093/bioinformatics/btab241.
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Abstract Motivation Modeling microbiome systems as complex networks are known as the problem of network inference. Microbial association network inference is of great significance in applications on clinical diagnosis, disease treatment, pathological analysis, etc. However, most current network inference methods focus on mining strong pairwise associations between microorganisms, which is defective in reflecting the comprehensive interactive patterns participated by multiple microorganisms. It is also possible that the microorganisms involved in the generated network are not dominant in the microbiome due to the mere focus on the strength of pairwise associations. Some scholars tried to mine comprehensive microbial associations by association rule mining methods, but the adopted algorithms are relatively basic and have severe limitations such as low calculation efficiency, lacking the ability of mining negative correlations and high redundancy in results, making it difficult to mine high-quality microbial association rules and accurately infer microbial association networks. Results We proposed a microbial association network inference method ‘MANIEA’ based on the improved Eclat algorithm for mining positive and negative microbial association rules. We also proposed a new method for transforming association rules into microbial association networks, which can effectively demonstrate the co-occurrence and causal correlations in association rules. An experiment was conducted on three authentic microbial abundance datasets to compare the ‘MANIEA’ with currently popular network inference methods, which demonstrated that the proposed ‘MANIEA’ show advantages in aspects of correlation forms, computation efficiency, adjustability and network characteristics. Availability and implementation The algorithms and data are available at: https://github.com/MaidiL/MANIEA.
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Shaikh, Mateen, and Joseph Beyene. "Testing genotypes-phenotype relationships using permutation tests on association rules." Statistical Applications in Genetics and Molecular Biology 14, no. 1 (2015). http://dx.doi.org/10.1515/sagmb-2014-0033.
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AbstractAssociation rule mining is a knowledge discovery technique which informs researchers about relationships between variables in data. These relationships can be focused to a specific set of response variables. We propose an augmented version of this method to discover groups of genotypes which relate to specific outcomes. We derive the methodology to find these candidate groups of genotypes and illustrate how the method works on data regarding neuroinvasive complications of West Nile virus and through simulation.
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Yu, Hui. "Online teaching quality evaluation based on emotion recognition and improved AprioriTid algorithm." Journal of Intelligent & Fuzzy Systems, December 17, 2020, 1–11. http://dx.doi.org/10.3233/jifs-189534.
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The association rule algorithm in data mining is used to study the factors that may affect students’ performance, to make suggestions for teaching work, and to provide decision-making basis for teachers and teaching administrators, which has practical significance. There are many potential applications for facial expression recognition technology. For example, in the teaching process, facial expression recognition technology helps teachers understand students and judge students’ reactions to certain things. Based on the current research status of emotion recognition and data mining algorithms, this paper improves the AprioriTid algorithm and constructs an online teaching quality evaluation model based on teaching needs. In addition, this article applies the model constructed in this article to the evaluation of English online teaching quality and evaluates teaching quality through data mining. The experimental research shows that the model constructed in this paper has good performance.
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Park, Chihyun, Jean R. Clemenceau, Anna Seballos, et al. "A spatiotemporal analysis of opioid poisoning mortality in Ohio from 2010 to 2016." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-83544-y.
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AbstractOpioid-related deaths have severely increased since 2000 in the United States. This crisis has been declared a public health emergency, and among the most affected states is Ohio. We used statewide vital statistic data from the Ohio Department of Health (ODH) and demographics data from the U.S. Census Bureau to analyze opioid-related mortality from 2010 to 2016. We focused on the characterization of the demographics from the population of opioid-related fatalities, spatiotemporal pattern analysis using Moran’s statistics at the census-tract level, and comorbidity analysis using frequent itemset mining and association rule mining. We found higher rates of opioid-related deaths in white males aged 25–54 compared to the rest of Ohioans. Deaths tended to increasingly cluster around Cleveland, Columbus and Cincinnati and away from rural regions as time progressed. We also found relatively high co-occurrence of cardiovascular disease, anxiety or drug abuse history, with opioid-related mortality. Our results demonstrate that state-wide spatiotemporal and comorbidity analysis of the opioid epidemic could provide novel insights into how the demographic characteristics, spatiotemporal factors, and/or health conditions may be associated with opioid-related deaths in the state of Ohio.
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Dinar, Mahmoud, Andreea Danielescu, Christopher MacLellan, Jami J. Shah, and Pat Langley. "Problem Map: An Ontological Framework for a Computational Study of Problem Formulation in Engineering Design." Journal of Computing and Information Science in Engineering 15, no. 3 (2015). http://dx.doi.org/10.1115/1.4030076.
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Studies of design cognition often face two challenges. One is a lack of formal cognitive models of design processes that have the appropriate granularity: fine enough to distinguish differences among individuals and coarse enough to detect patterns of similar actions. The other is the inadequacies in automating the recourse-intensive analyses of data collected from large samples of designers. To overcome these barriers, we have developed the problem map (P-maps) ontological framework. It can be used to explain design thinking through changes in state models that are represented in terms of requirements, functions, artifacts, behaviors, and issues. The different ways these entities can be combined, in addition to disjunctive relations and hierarchies, support detailed modeling and analysis of design problem formulation. A node–link representation of P-maps enables one to visualize how a designer formulates a problem or to compare how different designers formulate the same problem. Descriptive statistics and time series of entities provide more detailed comparisons. Answer set programming (ASP), a predicate logic formalism, is used to formalize and trace strategies that designers adopt. Data mining techniques (association rule and sequence mining) are used to search for patterns among large number of designers. Potential uses of P-maps are computer-assisted collection of large data sets for design research, development of a test for the problem formulation skill, and a tutoring system.
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Sangeetha J., Prof, Jegatheesh B. S., Balaji B, and Hemnath N. "Credit Card Fraud Detection Using Fuzzy Rule Based Classifier." International Journal of Advanced Research in Science, Communication and Technology, June 30, 2020, 41–44. http://dx.doi.org/10.48175/ijarsct-99.
Full textAbstract:
Fraud detection is an emerging topic of notable importance. Data mining strategies have been applied most considerably to the detection of insurance fraud, monetary fraud and financial fraud. This project will mainly focus on detecting fraudulent credit card transactions. Fraud detection in telecommunication systems, particularly the case of extraordinary imposed fraud, providing an anomaly detection technique supported by way of a signature schema, fraud deals with cases regarding criminal purposes that typically are different to identify, have additionally attracted a a tremendous deal of attention in latest years. The use of credit cards has dramatically increased because of a fast advancement inside the electronic commerce technology. Credit card will become the most popular mode of payment for each on line as properly as ordinary purchase, in instances of fraud related to it are also growing day through day. In this research sequence of operations in credit card transaction processing using a Fuzzy rule based classifier and accuracy is improved in the detection of frauds compared to other algorithms. A Naïve Bayes is initially trained with the everyday behaviour of a card holder. If an incoming credit card transaction is not accepted by the trained version with sufficiently excessive probability, it’s considered to be fraudulent. At the same time, it ensures that true transactions aren’t rejected. Supervised learning requires prior type to anomalies. In this research fuzzy rule primarily based category set of rules used for modelling real world credit card information statistics and detecting the anomaly usage of credit card information’s. Whenever anomaly credit card usage detected the system will capture the anomaly user face and freeze the anomaly user system. Django framework is used for web app creation.