THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

Association rule learning(ARL)is a widely used technique for discovering relationships within datasets.However,it often generates excessive irrelevant or ambiguous rules.Therefore,post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors.Recently,several post-processing methods have been proposed,each with its own strengths and weaknesses.In this paper,we propose THAPE(Tunable Hybrid Associative Predictive Engine),which combines descriptive and predictive techniques.By leveraging both techniques,our aim is to enhance the quality of analyzing generated rules.This includes removing irrelevant or redundant rules,uncovering interesting and useful rules,exploring hidden association rules that may affect other factors,and providing backtracking ability for a given product.The proposed approach offers a tailored method that suits specific goals for retailers,enabling them to gain a better understanding of customer behavior based on factual transactions in the target market.We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness.Through this application,we successfully mined a concise set of highly interesting and useful association rules.Out of the 11,265 rules generated,we identified 125 rules that are particularly relevant to the business context.These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.

Discovering hidden patterns:Association rules for cardiovascular diseases in type 2 diabetes mellitus

BACKGROUND It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus(T2DM)and coronary artery disease(CAD),and studies are able to correlate their relationships with available biological and clinical evidence.The aim of the current study was to apply association rule mining(ARM)to discover whether there are consistent patterns of clinical features relevant to these diseases.ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care.AIM To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery.METHODS This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi,involving a total of 300 consented subjects categorized into three groups:CAD with diabetes,CAD without diabetes,and healthy controls,with 100 subjects in each group.The participants were enrolled from the Cardiology IPD&OPD for the sample collection.The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value.RESULTS The clinical dataset comprised 35 attributes from enrolled subjects.The analysis produced rules with a maximum branching factor of 4 and a rule length of 5,necessitating a 1%probability increase for enhancement.Prominent patterns emerged,highlighting strong links between health indicators and diabetes likelihood,particularly elevated HbA1C and random blood sugar levels.The ARM technique identified individuals with a random blood sugar level>175 and HbA1C>6.6 are likely in the“CAD-with-diabetes”group,offering valuable insights into health indicators and influencing factors on disease outcomes.CONCLUSION The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes.Implying artificial intelligence techniques with medical data,we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.

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

This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.

Quantum Algorithm for Mining Frequent Patterns for Association Rule Mining

Maximum frequent pattern generation from a large database of transactions and items for association rule mining is an important research topic in data mining. Association rule mining aims to discover interesting correlations, frequent patterns, associations, or causal structures between items hidden in a large database. By exploiting quantum computing, we propose an efficient quantum search algorithm design to discover the maximum frequent patterns. We modified Grover’s search algorithm so that a subspace of arbitrary symmetric states is used instead of the whole search space. We presented a novel quantum oracle design that employs a quantum counter to count the maximum frequent items and a quantum comparator to check with a minimum support threshold. The proposed derived algorithm increases the rate of the correct solutions since the search is only in a subspace. Furthermore, our algorithm significantly scales and optimizes the required number of qubits in design, which directly reflected positively on the performance. Our proposed design can accommodate more transactions and items and still have a good performance with a small number of qubits.

Study on association rules mining based on semantic relativity

An association rules mining method based on semantic relativity is proposed to solve the problem that there are more candidate item sets and higher time complexity in traditional association rules mining.Semantic relativity of ontology concepts is used to describe complicated relationships of domains in the method.Candidate item sets with less semantic relativity are filtered to reduce the number of candidate item sets in association rules mining.An ontology hierarchy relationship is regarded as a directed acyclic graph rather than a hierarchy tree in the semantic relativity computation.Not only direct hierarchy relationships,but also non-direct hierarchy relationships and other typical semantic relationships are taken into account.Experimental results show that the proposed method can reduce the number of candidate item sets effectively and improve the efficiency of association rules mining.

Fast FP-Growth for association rule mining

In this paper, we propose an efficient algorithm, called FFP-Growth (shortfor fast FP-Growth) , to mine frequent itemsets. Similar to FP-Growth, FFP-Growth searches theFP-tree in the bottom-up order, but need not construct conditional pattern bases and sub-FP-trees,thus, saving a substantial amount of time and space, and the FP-tree created by it is much smallerthan that created by TD-FP-Growth, hence improving efficiency. At the same time, FFP-Growth can beeasily extended for reducing the search space as TD-FP-Growth (M) and TD-FP-Growth (C). Experimentalresults show that the algorithm of this paper is effective and efficient.

Application Comparison of Association Rules and C4.5 Rules in Land Evaluation

Association rules and C4.5 rules can overcome the shortage of the traditional land evaluation methods and improve the intelligibility and efficiency of the land evaluation knowledge.In order to compare these two kinds of classification rules in the application,two fuzzy classifiers were established by combining with fuzzy decision algorithm especially based on Second General Soil Survey of Guangdong Province.The results of experiments demonstrated that the fuzzy classifier based on association rules obtain a higher accuracy rate,but with more complex calculation process and more computational overhead;the fuzzy classifier based on C4.5 rules obtain a slightly lower accuracy,but with fast computation and simpler calculation.

The Application of Weighted Association Rules in Host-Based Intrusion Detection System

Association rules are useful for determining correlations between items. Applying association rules to intrusion detection system (IDS) can improve the detection rate, but false positive rate is also increased. Weighted association rules are used in this paper to mine intrustion models, which can increase the detection rate and decrease the false positive rate by some extent. Based on this, the structure of host-based IDS using weighted association rules is proposed.

A Survey on Methods and Applications of Intelligent Market Basket Analysis Based on Association Rule

The market trends rapidly changed over the last two decades.The primary reason is the newly created opportunities and the increased number of competitors competing to grasp market share using business analysis techniques.Market Basket Analysis has a tangible effect in facilitating current change in the market.Market Basket Analysis is one of the famous fields that deal with Big Data and Data Mining applications.MBA initially uses Association Rule Learning(ARL)as a mean for realization.ARL has a beneficial effect in providing a plenty benefit in analyzing the market data and understanding customers’behavior.An important motive of using such techniques is maximizing the business profit as well as matching the exact customer needs as closely as possible.In this survey paper,we discussed several applications and methods of MBA based on ARL.Also,we reviewed some association rule learning measurements including trust,lift,leverage,and others.Furthermore,we discuss some open issues and future topics in the area of market basket analysis and association rule learning.

Finding Main Causes of Elevator Accidents via Multi-Dimensional Association Rule in Edge Computing Environment

In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.

Customer Requirements Mapping Method Based on Association Rule Mining for Mass Customization

Customer requirements analysis is the key step for product variety design of mass customiza-tion(MC). Quality function deployment (QFD) is a widely used management technique for understanding the voice of the customer (VOC), however, QFD depends heavily on human subject judgment during extracting customer requirements and determination of the importance weights of customer requirements. QFD pro-cess and related problems are so complicated that it is not easily used. In this paper, based on a general data structure of product family, generic bill of material (GBOM), association rules analysis was introduced to construct the classification mechanism between customer requirements and product architecture. The new method can map customer requirements to the items of product family architecture respectively, accomplish the mapping process from customer domain to physical domain directly, and decrease mutual process between customer and designer, improve the product design quality, and thus furthest satisfy customer needs. Finally, an example of customer requirements mapping of the elevator cabin was used to illustrate the proposed method.

Mining association rules in incomplete information systems

Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.

Mining multilevel spatial association rules with cloud models

The traditional generalization-based knowledge discovery method is introduced. A new kind of multilevel spatial association of the rules mining method based on the cloud model is presented. The cloud model integrates the vague and random use of linguistic terms in a unified way. With these models, spatial and nonspatial attribute values are well generalized at multiple levels, allowing discovery of strong spatial association rules. Combining the cloud model based method with Apriori algorithms for mining association rules from a spatial database shows benefits in being effective and flexible.

A New Method Based on Association Rules Mining and Geo-filter for Mining Spatial Association Knowledge

Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.

CARM:Context Based Association Rule Mining for Conventional Data

This paper is aimed to develop an algorithm for extracting association rules,called Context-Based Association Rule Mining algorithm(CARM),which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm(CBPNARM).CBPNARM was developed to extract positive and negative association rules from Spatiotemporal(space-time)data only,while the proposed algorithm can be applied to both spatial and non-spatial data.The proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative associations.Many association rules related to sustainable energy development are extracted by the proposed algorithm that needs to be pruned by some pruning technique.The context,in this paper serves as a pruning measure to extract pertinent association rules from non-spatial data.Conditional Probability Increment Ratio(CPIR)is also added in the proposed algorithm that was not used in CBPNARM.The inclusion of the context variable and CPIR resulted in fewer rules and improved robustness and ease of use.Also,the extraction of a common negative frequent itemset in CARM is different from that of CBPNARM.The rules created by the proposed algorithm are more meaningful,significant,relevant and insightful.The accuracy of the proposed algorithm is compared with the Apriori,PNARM and CBPNARM algorithms.The results demonstrated enhanced accuracy,relevance and timeliness.

Regression Analysis of the Number of Association Rules

The typical model, which involves the measures： support, confidence, and interest, is often adapted to mining association rules. In the model, the related parameters are usually chosen by experience; consequently, the number of useful rules is hard to estimate. If the number is too large, we cannot effectively extract the meaningful rules. This paper analyzes the meanings of the parameters and designs a variety of equations between the number of rules and the parameters by using regression method. Finally, we experimentally obtain a preferable regression equation. This paper uses multiple correlation coeficients to test the fitting efiects of the equations and uses significance test to verify whether the coeficients of parameters are significantly zero or not. The regression equation that has a larger multiple correlation coeficient will be chosen as the optimally fitted equation. With the selected optimal equation, we can predict the number of rules under the given parameters and further optimize the choice of the three parameters and determine their ranges of values.

Association Rule Discovery and Its Applications

Data mining, i.e., mining knowledge from large amounts of data, is a demanding field since huge amounts of data have been collected in various applications. The collected data far exceed people's ability to analyze it. Thus, some new and efficient methods are needed to discover knowledge from large database. Association rule discovery is an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent item sets and then forming conditional implication rules among them. In this paper, we describe and summarize recent work on association rule discovery, offer a new method to association rule mining and point out that association rule discovery can be applied in spatial data mining. It is useful to discover knowledge from remote sensing and geographical information system.

A New Hybrid Algorithm for Association Rule Mining

HA （hashing array）, a new algorithm, for mining frequent itemsets of large database is proposed. It employs a structure hash array, ltemArray （ ） to store the information of database and then uses it instead of database in later iteration. By this improvement, only twice scanning of the whole database is necessary, thereby the computational cost can be reduced significantly. To overcome the performance bottleneck of frequent 2-itemsets mining, a modified algorithm of HA, DHA （directaddressing hashing and array） is proposed, which combines HA with direct-addressing hashing technique. The new hybrid algorithm, DHA, not only overcomes the performance bottleneck but also inherits the advantages of HA. Extensive simulations are conducted in this paper to evaluate the performance of the proposed new algorithm, and the results prove the new algorithm is more efficient and reasonable.

AN EVALUATION APPROACH FOR THE PROGRAM OF ASSOCIATION RULES ALGORITHM BASED ON METAMORPHIC RELATIONS

As data mining more and more popular applied in computer system,the quality as-surance test of its software would be get more and more attention.However,because of the ex-istence of the 'oracle' problem,the traditional test method is not ease fit for the application program in the field of the data mining.In this paper,based on metamorphic testing,a software testing method is proposed in the field of the data mining,makes an association rules algorithm as the specific case,and constructs the metamorphic relation on the algorithm.Experiences show that the method can achieve the testing target and is feasible to apply to other domain.

The research on rule of Acupoints and Massage Manipulations selection for Post-ischemic Stroke Constipation based on association rule and entropy clustering analysis

Constipation is a common complication of stroke,and it is increasing year by year,which is worthy of attention.In fact,as an effective treatment for Post-ischemic Stroke Constipation,massage has been recognized by doctors at home and abroad.However,In the known research reports,massage prescriptions are complicated,therefore,a simple and effective massage prescription is urgently needed to effectively guide the clinic and promote it.In this study,we used association rule and entropy clustering analysis methods to mine clinical literature on Post-ischemic Stroke Constipation in 7 databases,and combined with data analysis,traditional chinese massage theory and clinical practice,a core new prescription is summarized.The core new prescription of massage in treating Post-ischemic Stroke Constipation take tonifying spleen,nourishing Qi and generating Body Fluid,promoting Qi,invigorating the circulation of blood and eliminating phlegm as the principle of treatment,which is accord with the pathogenesis of this disease,can better guide the clinical practice and facilitate the popularization and application of massage therapy.