Rule Mining Algorithm Research Articles

WOMDI-Apriori Data Mining Algorithm for Clustered Indicators Analysis of Specialty Groups in Higher Vocational Colleges

The cluster effect of specialty groups plays an important role in the development of Higher Vocational Colleges. The purpose of this research is to scientifically explore the interaction mech- anism of specialty groups clustering indexes in higher vocational colleges, uantitatively analyze the correlation of these indexes, nd explore reasonable measures to promote the specialty groups clustering effect in higher ocational olleges. Firstly, data denoising and field screening were car- ried out on the original data, and then the variables were clustered and divided into LHS (Left Hand Side) and RHS (Right Hand Side). Then, an improved multi-dimensional interactive Apri- ori association rule mining algorithm considering index weights and orientation constraints was proposed. The improved Apriori algorithm and the traditional Apriori algorithm were applied to mine the structured data sets. The results show that the improved WOMDI-Apriori algorithm in this study improves the accuracy by 79.96% compared with the traditional Apriori algorithm. The results indicate that, when the indicators of brand, key and characteristic majors at or above the provincial level, proportion of full-time teachers with double qualifications, and the number of internship students accepted by cooperative enterprises are at a low level, the number of projects and satisfaction proportion of employers with graduates would be negatively affected; The majr category of equipment manufacturing is subjected to various factors coupling, which may lead to different graduates’ counterpart mployment rate; for association rules where the uccessor of the mining results is dominated by negative results, measures should be taken to avoid or reduce the possibility of their occurrence as much as possible. For association rules in which the successors of the mining results are dominated by positive results, measures should be taken to facilitate the occurrence of these frequent item sets whenever possible. The framework proposed in this research can provide theoretical guidance for analyzing operating characteristics and promoting the positive effects of specialty groups in higher vocational colleges.

CANCER PREDICTION IN INFLAMMATORY BOWEL DISEASE PATIENTS BY USING MACHINE LEARNING ALGORITHMS

Colon cancer is one of the most common spread cancers in the world, which leads to total death of 10%. Prediction of onset of cancer, and the cause of its development in these patients can be of an enormous help and relief to those affected, as they can get back their “normal” life. Data mining and machine learning are important intelligent tools for classification, prediction and hidden relation extraction between patient information. We collected data from Shahid Faghihi Hospital in Shiraz. Features collected are as follows: Gender, age, duration of cancer before surgery, number of times the patients used bathroom, taking anti-inflammatory drug prednisolone, duration of drug use and dosage, kind of surgery and number of times consulted and retreatment of surgery, incontinence, etc. After pre-processing and data cleaning stages, effective features were extracted, and also occurrence of cancer predicts by using different classification algorithms. Then association rule mining algorithms like Apriori were used for obtaining any internal hidden relation between entries. Approaching them with different algorithms and assessing them with support vector machine was with highest prediction accuracy (84%). Due to unbalanced dataset, we chose cost sensitive support vector machine. In another aspect, after applying Apriori algorithm, the conditions of non-inflammation were extracted based on dataset features. Some significant outcomes are in what follows. If surgery treatment or diagnosed was less than 5 years, the possibility of developing colon cancer is lower. Also, as the duration of disease increases, the possibility of reoperation increases, as confirmed by the interiors. Since this issue with these features was raised for the first time in this paper at the suggestion of internists, early detection of cancer and also the extraction of effective laws can be of help to the medical community. In future, to get higher accuracy, the improvement of the dataset in terms of number of samples and colonoscopy image features is considered.

New Spark solutions for distributed frequent itemset and association rule mining algorithms

AbstractThe large amount of data generated every day makes necessary the re-implementation of new methods capable of handle with massive data efficiently. This is the case of Association Rules, an unsupervised data mining tool capable of extracting information in the form of IF-THEN patterns. Although several methods have been proposed for the extraction of frequent itemsets (previous phase before mining association rules) in very large databases, the high computational cost and lack of memory remains a major problem to be solved when processing large data. Therefore, the aim of this paper is three fold: (1) to review existent algorithms for frequent itemset and association rule mining, (2)to develop new efficient frequent itemset Big Data algorithms using distributive computation, as well as a new association rule mining algorithm in Spark, and (3) to compare the proposed algorithms with the existent proposals varying the number of transactions and the number of items. To this purpose, we have used the Spark platform which has been demonstrated to outperform existing distributive algorithmic implementations.

A Comparative Study on Association Rule Mining Algorithms on the Hospital Infection Control Dataset

Administrative procedures in various organizations produce numerous crucial records and data. These records and data are also used in other processes like customer relationship management and accounting operations.It is incredibly challenging to use and extract valuable and meaningful information from these data and records because they are frequently enormous and continuously growing in size and complexity.Data mining is the act of sorting through large data sets to find patterns and relationships that might aid in the data analysis process of resolving business issues. Using data mining techniques, enterprises can forecast future trends and make better business decisions.The Apriori algorithm has been introduced to calculate the association rules between objects; the primary goal of this algorithm is to establish an association rule between various things. The association rule describes how two or more objects are related.We have employed the Apriori property and Apriori Mlxtend algorithms in this study and we applied them on the hospital database; and, by using python coding, the results showed that the performance of Apriori Mlxtend was faster, and it was 0.38622, and the Apriori property algorithm was 0.090909. That means the Apriori Mlxtend was better than the Apriori property algorithm.

A Hyper-Personalized Product Recommendation System Focused on Customer Segmentation: An Application in the Fashion Retail Industry

Providing the right products, at the right place and time, according to their customer’s preferences, is a problem-seeking solution, especially for companies operating in the retail industry. This study presents an integrated product RS that combines various data mining techniques with this motivation. The proposed approach consists of the following steps: (1) customer segmentation; (2) adding the location dimension and determining the association rules; (3) the creation of product recommendations. We used the RFM technique for customer segmentation and the k-means clustering algorithm to create customer segments with customer-based RFM values. Then, the Apriori algorithm, one of the association rule mining algorithms, is used to create accurate rules. In this way, cluster-based association rules are created. Finally, product recommendations are presented with a rule-based heuristic algorithm. This is the first system that considers customers’ demographic data in the fashion retail industry in the literature. Furthermore, the customer location information is used as a parameter for the first time for the clustering phase of a fashion retail product RS. The proposed systematic approach is aimed at producing hyper-personalized product recommendations for customers. The proposed system is implemented on real-world e-commerce data and compared with the current RSs used according to well-known metrics and the average sales information. The results show that the proposed system provides better values.

Ocular disease examination of fundus images by hybriding SFCNN and rule mining algorithms

ABSTRACT Eye is the organ of vision, which is necessary for daily routine, disorders can prevent it from working effectively. Human eye can be harmed, including age, strain, poor care, and using medications without a prescription. Choroid behind the retina are affected by retinal disorders. Small and big blood vessels compressed densely and affected by light, in order to perform multi-label categories of fundus images and properly handle the issue of interleaved overflow in fundus image lesions. We proposed Single population leapfrog optimization convolutional neural network algorithm (SFCNN) and Rule mining algorithm for self-closing computer investigation of ocular disease related to retinal images, which makes the work easier for ophthalmologists to state the state of the disease. For classification hybrid techniques SFCNN and EARMAM are proposed to predict the diseased image. When comparing with existing approach, proposed approach yields an accuracy of 98.75%, a sensitivity of 97%, and specificity of 98%.

Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches

Hepatocellular carcinoma (HCC) is the most frequent type of primary liver cancer. Early-stage detection plays an essential role in making treatment decisions and identifying dominant molecular mechanisms. We utilized machine learning algorithms to find significant mRNAs and microRNAs (miRNAs) at the early and late stages of HCC. First, pre-processing approaches, including organization, nested cross-validation, cleaning, and normalization were applied. Next, the t-test/ANOVA methods and binary particle swarm optimization were used as a filter and wrapper method in the feature selection step, respectively. Then, classifiers, based on machine learning and deep learning algorithms were utilized to evaluate the discrimination power of selected features (mRNAs and miRNAs) in the classification step. Finally, the association rule mining algorithm was applied to selected features for identifying key mRNAs and miRNAs that can help decode dominant molecular mechanisms in HCC stages. The applied methods could identify key genes associated with the early (e.g., Vitronectin, thrombin-activatable fibrinolysis inhibitor, lactate dehydrogenase D (LDHD), miR-590) and late-stage (e.g., SPRY domain containing 4, regucalcin, miR-3199-1, miR-194-2, miR-4999) of HCC. This research could establish a clear picture of putative candidate genes, which could be the main actors at the early and late stages of HCC.

Dynamic Weights Based Risk Rule Generation Algorithm for Incremental Data of Customs Declarations

Aimed at shortcomings, such as fewer risk rules for assisting decision-making in customs entry inspection scenarios and relying on expert experience generation, a dynamic weight assignment method based on the attributes of customs declaration data and an improved dynamic-weight Can-Tree incremental mining algorithm are proposed. In this paper, we first discretize the customs declaration data, and then form composite attributes by combining and expanding the attributes, which is conducive to generating rules with risk judgment significance. Then, weights are determined according to the characteristics and freshness of the customs declaration data, and the weighting method is applied to the Can-Tree algorithm for incremental association rule mining to automatically and efficiently generate risk rules. By comparing FP-Growth and traditional Can-Tree algorithms experimentally, the improved dynamic-weight Can-Tree incremental mining algorithm occupies less memory space and is more time efficient. The introduction of dynamic weights can visually distinguish the importance level of customs declaration data and mine more representative rules. The dynamic weights combine confidence and elevation to further improve the accuracy and positive correlation of the generated rules.

Parallel incremental association rule mining framework for public opinion analysis

Internet public opinion association rule mining (POARM) has garnered significant attention from a larger group of netizens. However, most POARM methods have been applied to post-event analysis, which has poor timeliness and a low efficiency. Therefore, the real-time monitoring of public opinion association rules is lacking. To address this problem, we propose the parallel Incremental POARM Framework (IPOARM), which improves the timeliness of association rule mining in two ways: 1) using an incremental merge method to consider both inserted and deleted public opinion transaction sets and reuse previous frequent itemsets to reduce redundant computation and 2) employing a parallel implementation of big data process platforms. Moreover, the flexible association rule mining (ARM) algorithm selection structure of IPOARM enables users to freely select suitable ARM algorithms. We represent four classic transaction sets as public opinion transaction sets and compare the IPOARM framework with two novel incremental association rules mining algorithms. Our evaluations indicate that the IPOARM framework can discover Internet public opinion association rules quickly, implying that it can be easily integrated into existing big data processing platforms and that it significantly improves the mining accuracy and efficiency by 12.756% and 29.371%, respectively.

COVID-19 Data Analysis with a Multi-Objective Evolutionary Algorithm for Causal Association Rule Mining

Association rule mining plays a crucial role in the medical area in discovering interesting relationships among the attributes of a data set. Traditional association rule mining algorithms such as Apriori, FP growth, or Eclat require considerable computational resources and generate large volumes of rules. Moreover, these techniques depend on user-defined thresholds which can inadvertently cause the algorithm to omit some interesting rules. In order to solve such challenges, we propose an evolutionary multi-objective algorithm based on NSGA-II to guide the mining process in a data set composed of 15.5 million records with official data describing the COVID-19 pandemic in Mexico. We tested different scenarios optimizing classical and causal estimation measures in four waves, defined as the periods of time where the number of people with COVID-19 increased. The proposed contributions generate, recombine, and evaluate patterns, focusing on recovering promising high-quality rules with actionable cause–effect relationships among the attributes to identify which groups are more susceptible to disease or what combinations of conditions are necessary to receive certain types of medical care.

New Rule-Based Model to Predict Maternal Mortality Risk

Most maternal deaths occur by preventable means [1]. This could be addressed by finding better ways to track the vitals of mothers and better ways of determining maternal mortality risk. This paper uses an association rule mining (ARM) algorithm called Apriori to generate human readable rules to better classify maternal mortality risk. The Waikato Environment for Knowledge Analysis (WEKA) was used to apply the Apriori algorithm to the data set and python was used to clean the data set. The data set that was used consisted of real maternal health vitals that were then converted into categorical data to apply the Apriori algorithm.

Performance Analysis of Association Rule Mining Algorithms: Evidence from the Retailing Industry

Performance Analysis of Association Rule Mining Algorithms: Evidence from the Retailing Industry

The cleaning method of duplicate big data based on association rule mining algorithm

In order to overcome the problems of low cleaning efficiency and serious memory consumption in traditional large data cleaning methods, this paper proposes a new cleaning method of repeated big data based on association rule mining algorithm. This method uses association rule mining algorithm to obtain the frequent itemsets of repeated big data after repeated cycle calculation. At the same time, the output mode of the algorithm is optimised in parallel, and the Hadoop interface is modified to change the reading mode of MapReduce. The first frequent itemset is used to clean the repeated big data. The experimental results show that the proposed method can effectively reduce the execution time and memory consumption, and the shortest cleaning time is only 1.28 min, indicating the feasibility of the proposed method.

An Explorative Analysis of Innovation Activities in the Agricultural Sector: An Association Rule Mining Algorithm Approach

An Explorative Analysis of Innovation Activities in the Agricultural Sector: An Association Rule Mining Algorithm Approach

Use of data mining to establish associations between Indian marine fish catch and environmental data

For decades, changes in fish catch composition and the marine environment have been monitored worldwide and recorded in databases like FAO FishStatJ and the European Union Copernicus Marine Service. However, the complexity and high variability in the dataset makes it challenging to find meaningful information through conventional data analytical methods. Therefore, in this pilot data mining study, we employed association rule mining algorithms (Apriori, ECLAT, and FP-Growth) to find frequently occurring itemsets in the fish-catch composition and marine environment data of the west and east coasts of India during the past decade (2011-2020). Firstly, the inherent spatial and temporal variations in fish-catch composition and marine environment (sea surface temperature and chlorophyll) on the west and east coasts of India were statistically analyzed and described. Then, the data were preprocessed, selected, and transformed into categorical attributes. By applying the association rule mining algorithms written in the Python language in the Google Colab workspace, we obtained frequent itemsets of fish catch and marine environment with different levels of minimum support and confidence. The preliminary results showed linear and inverse associations between changes in the sea surface temperature, chlorophyll concentration, and major catch groups, such as anchovies, Indian oil sardine, Indian mackerel, hairtails, butterfish-pomfrets, Bombay duck, flatfish, tunas, giant tiger prawn, crabs, lobsters, and cephalopods. Among the tested data mining algorithms, FP-Growth was found to be more efficient and reliable in finding associations between the spatiotemporal dynamics of the marine environment and fish distribution and abundance. Therefore, it can be potentially used to support marine fisheries? resource assessment and management strategies after refinement.

E-Commerce Website Usability Analysis Using the Association Rule Mining and Machine Learning Algorithm

The overall effectiveness of a website as an e-commerce platform is influenced by how usable it is. This study aimed to find out if advanced web metrics, derived from Google Analytics software, could be used to evaluate the overall usability of e-commerce sites and identify potential usability issues. It is simple to gather web indicators, but processing and interpretation take time. This data is produced through several digital channels, including mobile. Big data has proven to be very helpful in a variety of online platforms, including social networking and e-commerce websites, etc. The sheer amount of data that needs to be processed and assessed to be useful is one of the main issues with e-commerce today as a result of the digital revolution. Additionally, on social media a crucial growth strategy for e-commerce is the usage of BDA capabilities as a guideline to boost sales and draw clients for suppliers. In this paper, we have used the KMP algorithm-based multivariate pruning method for web-based web index searching and different web analytics algorithm with machine learning classifiers to achieve patterns from transactional data gathered from e-commerce websites. Moreover, through the use of log-based transactional data, the research presented in this paper suggests a new machine learning-based evaluation method for evaluating the usability of e-commerce websites. To identify the underlying relationship between the overall usability of the eLearning system and its predictor factors, three machine learning techniques and multiple linear regressions are used to create prediction models. This strategy will lead the e-commerce industry to an economically profitable stage. This capability can assist a vendor in keeping track of customers and items they have viewed, as well as categorizing how customers use their e-commerce emporium so the vendor can cater to their specific needs. It has been proposed that machine learning models, by offering trustworthy prognoses, can aid in excellent usability. Such models might be incorporated into an online prognostic calculator or tool to help with treatment selection and possibly increase visibility. However, none of these models have been recommended for use in reusability because of concerns about the deployment of machine learning in e-commerce and technical issues. One problem with machine learning science that needs to be solved is explainability. For instance, let us say B is 10 and all the people in our population are even. The hash function’s behavior is not random since only buckets 0, 2, 4, 6, and 8 can be the value of h(x). However, if B = 11, we would find that 1/11th of the even integers is transmitted to each of the 11 buckets. The hash function would work well in this situation.

A Niching Augmented Evolutionary Algorithm for the Identification of Functional Dependencies in Complex Technical Infrastructures From Alarm Data

Functional dependencies in complex technical infrastructures can cause unexpected cascades of failures. Given the complexity and continuous evolution in time of the infrastructure architecture, the identification of functional dependencies using the classical methods of system decomposition and logic analysis is not feasible. Then, we consider the availability of databases of alarm messages and frame the identification of functional dependencies in complex technical infrastructures as an optimization problem whose objective is the maximization of a metric measuring the level of dependence among alarms. A niching-based evolutionary algorithm has been developed to sequentially evolve a population of candidate solutions (group of alarms), maintaining diversity among them thanks to the use of a mechanism of population augmentation. The proposed algorithm is applied to a synthetic database of alarms generated by a complex technical infrastructure simulation model and to a real large-scale database of alarms collected from the particle accelerator of European Organization for Nuclear Research. The proposed algorithm is shown able to identify functional dependencies and to overperform other approaches based on the use of association rule mining algorithms, in terms of capability of extracting rare rules and computational efficiency.

Retraction Note: Building the electronic evidence analysis model based on association rule mining and FP-growth algorithm

Retraction Note: Building the electronic evidence analysis model based on association rule mining and FP-growth algorithm

Retracted: Informatization Teaching Mode of Vision Sensor Digital Image and Fusion Association Rule Mining Algorithm

Retracted: Informatization Teaching Mode of Vision Sensor Digital Image and Fusion Association Rule Mining Algorithm

Exploiting Textual Information for Fake News Detection.

"Fake news" refers to the deliberate dissemination of news with the purpose to deceive and mislead the public. This paper assesses the accuracy of several Machine Learning (ML) algorithms, using a style-based technique that relies on textual information extracted from news, such as part of speech counts. To expand the already proposed styled-based techniques, a new method of enhancing a linguistic feature set is proposed. It combines Named Entity Recognition (NER) with the Frequent Pattern (FP) Growth association rule mining algorithm, aiming to provide better insight into the papers' sentence level structure. Recursive feature elimination was used to identify a subset of the highest performing linguistic characteristics, which turned out to align with the literature. Using pre-trained word embeddings, document embeddings and weighted document embeddings were constructed using each word's TF-IDF value as the weight factor. The document embeddings were mixed with the linguistic features providing a variety of training/test feature sets. For each model, the best performing feature set was identified and fine-tuned regarding its hyper parameters to improve accuracy. ML algorithms' results were compared with two Neural Networks: Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM). The results indicate that CNN outperformed all other methods in terms of accuracy, when companied with pre-trained word embeddings, yet SVM performs almost the same with a wider variety of input feature sets. Although style-based technique scores lower accuracy, it provides explainable results about the author's writing style decisions. Our work points out how new technologies and combinations of existing techniques can enhance the style-based approach capturing more information.