Rule Mining Model Research Articles

A fourfold-objective-based cloud privacy preservation model with proposed association rule hiding and deep learning assisted optimal key generation

ABSTRACT Numerous studies have been conducted in an attempt to preserve cloud privacy, yet the majority of cutting-edge solutions fall short when it comes to handling sensitive data. This research proposes a “privacy preservation model in the cloud environment”. The four stages of recommended security preservation methodology are “identification of sensitive data, generation of an optimal tuned key, suggested data sanitization, and data restoration”. Initially, owner’s data enters the Sensitive data identification process. The sensitive information in the input (owner’s data) is identified via Augmented Dynamic Itemset Counting (ADIC) based Associative Rule Mining Model. Subsequently, the identified sensitive data are sanitized via the newly created tuned key. The generated tuned key is formulated with new fourfold objective-hybrid optimization approach-based deep learning approach. The optimally tuned key is generated with LSTM on the basis of fourfold objectives and the new hybrid MUAOA. The created keys, as well as generated sensitive rules, are fed into the deep learning model. The MUAOA technique is a conceptual blend of standard AOA and CMBO, respectively. As a result, unauthorized people will be unable to access information. Finally, comparative evaluation is undergone and proposed LSTM+MUAOA has achieved higher values on privacy about 5.21 compared to other existing models.

Forecasting Customer Actions: Exploring Machine Learning Techniques for Behavior Analysis and Prediction

Modern modelling of today’s consumer behaviour is highly dependent on data mining which mines customers’ data in order to answer specific questions in time. This complexity and uncertainty relating to the consumer be-haviour makes it difficult for us to predict. Therefore, selection of appropri-ate methods is very important. Predictive models can be designed so as to identify specific group or individual actions and thus making them useful marketing tools. However, many models are overly simplistic for they leave out some of the essential factors hence leading to inaccurate forecasting out-comes. Consequently, instead of concentrating on customer behaviour-firm capital structure relationships only a strong association rule mining model should be created using online store data that effectively estimates customer response. Knowing their business clients is crucial when it comes to profitability by aligning the company’s earnings with their concerns. Artificial intelligence (AI) optimizes product location by clustering techniques targeting the right customers. It examines customer behaviour and buying trends utilizing Kaggle’s client membership card records such as Customer ID, age, gender, annual income, spending score among others. This includes basic market analytics based on demographic information like age or income split into groups, however big data superiority over traditional methods cannot be underestimated including machine learning applications such as Customer Seg-mentation Automated Methodology (CSAM), k-means clustering for user segmentation among others. This approach facilitates focused advertising campaigns and development and launch of new products aimed at particular consumer sections.

A Model for Customer Opinion Mining and Sentiment Classification using a Mixture of Experts Machine Learning Model

This paper presents customer opinion mining technique based on a Mixture of Experts (MoE) machine learning model. The approach allows a corpus from open source data repositories to be classified into positive, negative and neutral sentiments as the case may be and in a predictive manner. The results of simulations showed that the proposed MoE approach can effectively be used as a core tool in opinion mining and also serve in decision making by appropriate categorizations. In particular, it was found that the use of higher epoch sizes greatly enhances the performance of the MoE by reducing perplexity and error cost margins to appreciable levels. Thus, the MoE presents a promising candidate for customer opinion mining particularly in business product development environments.

A Text-Based Predictive Maintenance Approach for Facility Management Requests Utilizing Association Rule Mining and Large Language Models

Introduction: Due to the lack of labeled data, applying predictive maintenance algorithms for facility management is cumbersome. Most companies are unwilling to share data or do not have time for annotation. In addition, most available facility management data are text data. Thus, there is a need for an unsupervised predictive maintenance algorithm that is capable of handling textual data. Methodology: This paper proposes applying association rule mining on maintenance requests to identify upcoming needs in facility management. By coupling temporal association rule mining with the concept of semantic similarity derived from large language models, the proposed methodology can discover meaningful knowledge in the form of rules suitable for decision-making. Results: Relying on the large German language models works best for the presented case study. Introducing a temporal lift filter allows for reducing the created rules to the most important ones. Conclusions: Only a few maintenance requests are sufficient to mine association rules that show links between different infrastructural failures. Due to the unsupervised manner of the proposed algorithm, domain experts need to evaluate the relevance of the specific rules. Nevertheless, the algorithm enables companies to efficiently utilize their data stored in databases to create interpretable rules supporting decision-making.

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

Research on Artificial Intelligence Assisted Decision Making System in Higher Level Sports Training

Abstract From the perspective of process monitoring of sports training, this paper discusses the basic framework and decision-making function system composition for establishing a decision-making system for higher vocational sports training. Integrate students’ sports data to design a real-time monitoring program for sports training intensity, which forms the mapping relationship between physical fitness and exercise intensity. Utilize and improve the Apriori algorithm to create an association rule mining model for the physical fitness of higher vocational students. Propose using the Multi-Agent model to optimize the data information management of the decision-making system and combine it with the RBF neural network algorithm for sports training performance prediction. To analyze the impact of the sports training decision-making system, application examples are introduced. Examine how association rules are implemented in the decision-making system of higher vocational sports training. When the number of frames is 300, the error correction accuracy before and after the improvement of the Apriori algorithm is 79.14% and 94.23%, respectively. The feedback rate of students’ performance information is maintained at about 0.85 with the assistance of the sports training decision-making system improved by the algorithm. The improved sports training decision-making system is more practical.

FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops

1Malasowe, Bridget Ogheneovo,, 2Okpako, Abugor Ejaita, 3 , 4Ejeh, Patrick Ogholuwarami, 5Ojugo, Arnold Adimabua & 6 1Department of Computer Science, University of Delta, Agbor, Nigeria 2Department of Cyber Security, University of Delta, Agbor, Nigeria 3Department of Cyber Security, Faculty of Computing, Delta State University of Science and Technology, Ozoro. Nigeria. 4Department of Computer Science, Dennis Osadebay University, Anwai-Asaba, Nigeria 5 Department of Computer Science, Federal University of Petroleum Resources Effurun, Nigeria 6 Department of Computer Science, Federal University of Petroleum Resources Effurun, Nigeria E-mails: 1bridget.malasowe@unidel.edu.ng; 2ejaita.okpako@unidel.edu.ng; 3okpormd@dsust.edu.ng, 4 patrick.ejeh@dou.edu.ng; 5ojugo.arnold@fupre.edu.ng, 6ako.rita@fupre.edu.ng ABSTRACT Transaction data often is a true presentation of consumers’ buying behavior, stored as a set of relational records, which properly harnessed via mining – can help businesses improve their sales volume as a decision support system. Managing such a system can pose many issues to biz such as feature evolution, concept evolution, concept drift, and infinite data length – and often makes it impractical to effectively store such big-data. To curb this, previous studies have assumed data to be stationary in using associative rule mining. This has deprived such systems of the flexibility and adaptiveness required to handle the dynamics of concept drift that characterizes transaction datasets. Our study thus proposes a basket frequent pattern growth trained associative rule mining model to handle large data. The dataset was retrieved from the Delta-Mall Asaba and consists of 556,000 transaction consumer records. The model consists of six-layers, and yields the best result with a 0.1 value for both the confidence and support level(s) at a 94% accuracy, sensitivity of 87%, and a specificity of 32% with a 20-second convergence and processing time. Keywords: Big-Data Analysis, Feature Evolution, Inventory Model, Market Basket Analysis, Businesses, Transactional Data Streams, Concept Drift CISDI Journal Reference Format Malasowe, B.O., Okpako, A.E., Okpor, M.D., Ejeh, P.O., Ojugo, A.A. & Ako, R.E. (2024): FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 2, Pp 15-28. dx.doi.org/10.22624/AIMS/CISDI/V15N2P2-1. Available online at www.isteams.net/cisdijournal

FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops

Transaction data often is a true presentation of consumers’ buying behavior, stored as a set of relational records, which properly harnessed via mining – can help businesses improve their sales volume as a decision support system. Managing such a system can pose many issues to biz such as feature evolution, concept evolution, concept drift, and infinite data length – and often makes it impractical to effectively store such big-data. To curb this, previous studies have assumed data to be stationary in using associative rule mining. This has deprived such systems of the flexibility and adaptiveness required to handle the dynamics of concept drift that characterizes transaction datasets. Our study thus proposes a basket frequent pattern growth trained associative rule mining model to handle large data. The dataset was retrieved from the Delta-Mall Asaba and consists of 556,000 transaction consumer records. The model consists of six-layers, and yields the best result with a 0.1 value for both the confidence and support level(s) at a 94% accuracy, sensitivity of 87%, and a specificity of 32% with a 20-second convergence and processing time. Keywords: Big-Data Analysis, Feature Evolution, Inventory Model, Market Basket Analysis, Businesses, Transactional Data Streams, Concept Drift CISDI Journal Reference Format Malasowe, B.O., Okpako, E.A., Ashioba, N.O., Ejeh, P.O., Ojugo, A.A. & Ako, R.E. (2024): FePARM: The Frequency-Patterned Associative Rule Mining Framework on Consumer Purchasing-Pattern for Online Shops. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 2, Pp 1-14. dx.doi.org/10.22624/AIMS/CISDI/V15N2P2. Available online at www.isteams.net/cisdijournal

Exploring the Critical Factors of Biomass Pyrolysis for Sustainable Fuel Production by Machine Learning

The goal of this study is to use machine learning methodologies to identify the most influential variables and optimum conditions that maximize biochar, bio-oil, and biogas yields for slow pyrolysis. First, experimental results reported in 37 articles were compiled into a database. Then, an explainable machine learning approach, Shapley Additive exPlanations (SHAP), was employed to find the effects of descriptors on the targets, and it was found that higher biochar yields can be obtained at lower temperatures using biomass with low volatile matter and high ash content. Following that, decision tree classification was used to discover the variables leading to high levels of the targets, and the most generalizable path for high biogas yield was found to be where the maximum particle diameter was less than or equal to 6.5 mm and the temperature was greater than 912 K. Finally, association rule mining models were created to find associations of descriptors with very high levels of yields, and among many findings, it was discovered that biomass with larger particles cannot be converted into bio-oil efficiently. It was then concluded that machine learning methods can help to determine the best slow pyrolysis conditions for the production of renewable and sustainable biofuels.

An integrated risk assessment model for the multi-perspective vulnerability of distribution networks under multi-source heterogeneous data distributions

The reliability of distribution network transmission corridors are vulnerable to different risk factors in both internal operation parameters and surrounding environmental conditions, and thus pinpoint evaluations of these impacting factors can further enhance their safety. To this end, this paper proposes an integrated assessment method, that is the multi-dimensional assessment with integrated fuzzy association (MDATFA). In this model, healthiness and importance indices of transmission lines are both defined to reflect the self-robustness and potential damages, respectively, and therefore to enable a two-dimensional assessment to incorporate the multi-source heterogeneous inputs. Firstly, healthiness indices for environmental characteristics are analyzed. On the one hand, discrete input features are processed by the established association rule mining model with rare variables (ARMrv) to mine and diagnose rarely occurred elements, and their healthiness contribution values can then be calculated. On the other hand, continuous features are handled by the designed hierarchical probabilistic fuzzy systems (HPFS). The weight assignments of these two expert models are adjusted by a gating network. Secondly, the importance indices for electrical characteristics are calculated. Under the condition that electrical signal constraints are satisfied, the amount of load loss and lost users can be solved by the built distribution network fault recovery model which is according to heuristic rule and second-order cone (HRSO). Finally, the validity and robustness of this operational condition assessment model is verified by an empirical analysis in an actual 39-bus power distribution system.

An association rule mining model for the application of construction technologies during COVID-19

COVID −19 impedes construction productivity, increases costs and delays the project schedule, disrupts health and safety regulations, and reduces profit margins. To flatten the COVID −19 curve and continue operations amid the pandemic, construction companies are digitising construction activities, processes and procedures using technology. This article reports on a study examining the use of construction technologies by Nigerian surveyors through a questionnaire. The questionnaires were sent to more than 300 surveying firms based on snowballing technique. Association rule mining (ARM) was used to model the correlations between the different construction technologies. A total of 91 association rules were identified using 6 measurement matrices. The first 30 rules are presented in this study. This study contributes to the body of knowledge on the sustainability and post-pandemic redesign of surveying practise in terms of repositioning service delivery. The findings are of interest to solution and technology providers to meet market demand. From an ontological perspective, the key findings of this study can be applied to surveying practise in and outside Nigeria and to other professionals in the construction sector. The implication is that construction technologies in the Nigerian construction sector are still in their infancy.

Research on Fuzzy Temporal Event Association Mining Model and Algorithm

As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the temporal dimension. After restructuring temporal data and extracting fuzzy features out of information, a fuzzy temporal event association rule mining model as well as an algorithm was constructed. The proposed algorithm can fully extract the data features at each granularity level while preserving the original information and reducing the amount of computation. Furthermore, it is capable of efficiently mining the possible rules underlying different temporal data streams. In experiments, by comparing and analyzing stock trading data in different temporal granularities, the model and algorithm identify association events in disorder trading. This not only is valuable in identifying stock anomalies, but also provides a new theoretical tool for dealing with complex irregular temporal data.

Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

Opinion Mining (OM) studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide. Though the interest in OM studies in the Arabic language is growing among researchers, it needs a vast number of investigations due to the unique morphological principles of the language. Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features. The comparative OM studies in the English language are wide and novel. But, comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage. The unique features of the Arabic language make it essential to expand the studies regarding the Arabic text. It contains unique features such as diacritics, elongation, inflection and word length. The current study proposes a Political Optimizer with Probabilistic Neural Network-based Comparative Opinion Mining (POPNN-COM) model for the Arabic text. The proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social media. Initially, the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful format. Then, the pre-processed data is fed into the PNN model for classification and recognition of the data under different class labels. At last, the PO algorithm is employed for fine-tuning the parameters involved in this model to achieve enhanced results. The proposed POPNN-COM model was experimentally validated using two standard datasets, and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.

Sentence opinion mining model for fusing target entities in official government documents

<abstract> <p>When drafting official government documents, it is necessary to firmly grasp the main idea and ensure that any positions stated within the text are consistent with those in previous documents. In combination with the field's demands, By taking advantage of suitable text-mining techniques to harvest opinions from sentences in official government documents, the efficiency of official government document writers can be significantly increased. Most existing opinion mining approaches employ text classification methods to directly mine the sentential text of official government documents while disregarding the influence of the objects described within the documents (i.e., the target entities) on the sentence opinion categories. To address these issues, this study proposes a sentence opinion mining model that fuses the target entities within documents. Based on the Bi-directional long short-term (BiLSTM) and attention mechanisms, the model fully considers the attention given by a official government document's target entity to different words within the corresponding sentence text, as well as the dependency between words of the sentence. The model subsequently fuses two by using feature vector fusion to obtain the final semantic representation of the text, which is then classified using a fully connected network and softmax function. Experimental results based on a dataset of official government documents show that the model significantly outperforms baseline models such as Text-convolutional neural network (TextCNN), recurrent neural network (RNN), and BiLSTM.</p> </abstract>

Analysis of Design Change Mechanism in Apartment Housing Projects Using Association Rule Mining (ARM) Model

Apartment housing occupies the highest proportion of the domestic construction market and significantly influences the flow of the real estate market. Frequent design changes and reconstruction in new apartment housing projects lead to an increase in construction cost and schedule, and a decline in design and construction quality, which is an important issue affecting the quality of use for occupants. The causal relationship of design changes and error in new apartment building projects has not been previously identified. Accordingly, design changes management activities in the construction phase using reactive manner are a critical risk that causes the productivity of the project to deteriorate. In this study, a complex and non-linear causal relationship between the design change factors was investigated using the association rule mining technique (ARM), a type of data mining technique. In particular, the associated relationship between design change factors that can be changed according to conditions that significantly affect the productivity and performance of projects, such as a contractor’s ranking in the field, contract price which means the project size, and contractor selection methods, was identified. The association rule between the design changes at the construction phase derived in this research can be used as a guide to identify and minimize the risk of design changes in advance.

An association rule mining model for evaluating the potential correlation of construction cross operation risk

PurposeCross operation is a common operation method in the building construction process nowadays. Due to the crossover, each other's operations are disturbed, and risks also interact. This superimposed relationship of risks is worthy of attention. The study aims to develop a model for analyzing cross-working risks. This model can quantify the correlation of various risk factors.Design/methodology/approachThe concept of cross operation and the cross types involved are clarified. The risk factors were extracted from cross-operation accidents. The association rule mining (ARM) was used to analyze the results of various cross-types accidents. With the help of visualization tools, the intensity distribution and correlation path of the relationship between each factor were obtained. A complete cross-operation risk analysis model was established.FindingsThe application of ARM method proves that there are obvious risk correlation deviations in different types of cross operations. A high-frequency risk common to all cross operations is on-site safety inspection and process supervision, but the subsequent problems are different. Cutting off the high-lift risk chain timely according to the results obtained by ARM can reduce or eliminate the danger of high-frequency risk factors.Originality/valueThis is the first systematic analysis of cross-work risk in the construction. The study determined the priority of risk management. The results contribute to targeted cross-work control to reduce accidents caused by cross-work.

Supervised Model for Sentiment Analysis Based on Hotel Review Clusters using RapidMiner

Customer feedback in the modern era like today is mostly presented in the form of digital reviews, including customer feedback at an inn or hotel, customer feedback is very valuable data where from this data the management can find out, identify and analyze the customer experience and what they need. With customer feedback in the form of digital reviews, it will allow a lot of data that can be obtained by hotel management and will provide many benefits if the data is processed correctly. To take advantage of large text review data, a combination of data mining and natural language processing techniques was chosen to process text in depth and efficiently. Text mining in the form of creating an opinion mining model using the Naïve Bayes classification algorithm is applied to find information and measure the main sentiments expressed in the reviewed text dataset, then the application of K-Means text grouping aims to group texts and get information about the main topics discussed from the content of the review dataset text in each group . By applying the constructed sentiment analysis model, approximately 90.90% accuracy results were obtained in reading texts and measuring sentiments related to hotel customer feedback data.

Classification rule mining based on Pareto-based Multiobjective Optimization

This paper introduces a novel classification rule mining model based on Pareto-based Multiobjective Optimization called CRM-PM. The process of rule extraction is a challenging classification task in data mining since it has several constraints and conflicting objectives such as accuracy and comprehensibility. In this study, this task is accepted as a multi-objective optimization problem. Classification accuracy and misclassification ratio are assigned as evaluation criteria. The candidate solutions are generated in the direction of a proposed strategy to determine optimal ranges of the attributes that form the rules. The proposed approach is applied on eight benchmark datasets (Iris Plants, Wine Quality, Glass Identification, Stat log (Heart), Haberman’s Survival, E-coli, Wisconsin Breast Cancer, and Pima Indians Diabetes) included in the University of California at Irvine machine learning repository. Furthermore, CRM-PM is run in three different validation modes: cross-validation, training without test data, and training with random splitting. Regarding experimental results, it can be said that the presented method has a promising capability for classification, and it achieves comparative or superior results.

A Dissolved Gas Assessment Model for Power Transformers According to Weighted Association Rule Mining

As one indispensable part of power systems, the reliable-operated power transformers are vital for energy transmission, whereas they are remarkably threatened by potential fault events. To achieve the satisfying and valid operation of power transformers, any fault events that may impact their health ought to be evaluated and early warned. With such motivations, this paper presents original insights on the assessment of power transmission health states via their internal dissolved gas, and an enhanced Association Rule Mining (ARM) model incorporating the analysis of High-Impact-Low-Probability (HILP) components, as well as a dynamic fault event risk evaluation approach, is proposed. The first step is to differentiate the risky components. Unlike the standard ARM, the rarely occurred components in each feature can also be assessed explicitly as the common components to explore the underlying HILP components in the proposed model, rather than just being viewed as trivial data and directly omitted. The second step is to rate the risk level of each risky component. A component importance measure-based evaluation approach is deployed to assess the corresponding risk weights of distinguished risky components. In this approach, the risk weight is determined straightforwardly via the impacts of each component on the variation level of total risks in the system, rather than simply by its frequency of occurrence or data share. Finally, the parameters of the risk weight evaluation approach can be dynamically adapted in an adjustment framework as well. This model is testified through an empirical case study, and the leading results can demonstrate its flexibility and robustness during real applications.

Development of argument based opinion mining model with sentimental data analysis from twitter content

AbstractIn present scenario, social networks have developed massive in practice and society impact. Specifically, micro‐blogging is on trend in various platforms, such as Twitter, Instagram to evaluate public opinions for various issues. In recent times, some methods are developed for evaluating Twitter messages, based on the sentiment and opinions presented in tweets, corresponding to the hash‐tags and keywords. However, these models have some issues in handling the contradictory content and inconsistent data. Considering with this, this article presents an argument based opinion mining model with sentimental data analysis, for extracting specific argument, which is assessed in bottom‐up manner from the content from society emotion's reflects on the messages. Moreover, this model makes the user to pull out the arguments from a document set, which contains content from commercial sites, to extract the mostly argued positive and negative content. This model use natural language processing techniques, extraction of argument words for defining the decisions. The classification Naive Bayes classification is used for categorizing the results widely under agreed or disagreed. The experimental results prove that the proposed model provides feasible and appropriate results in argument analysis from Twitter content.