Rare Association Rules Research Articles

Mining Association Rules on Time-sensitive Data

Real-world data is continuously produced andcollected quickly; hence, mining association rules are used tofind relationships between item sets. One of these combinedrule mining techniques is Rare Association Rule Mining(RARM), a method that extracts rare, combined rules with lowsupport but high confidence from the database. Additionally,a large amount of time-sensitive data is created in many fields,and combining valid and outdated data leads to low efficiencyin extracting combined rules. This paper explores a miningmethod that increases rare, combined rules on time-sensitivedata to extract common and rare patterns in the database.The primary approach of the research proposes a new treestructure called ILC-tree, inspired by the LC-tree structurepublished in 2022. Specifically, our approach includes a newtree structure and a fast reconstruction method. The proposedapproach reduces execution time and memory consumptionby approximately 2 times compared to the old tree structure.

Exploring the predictive factors of heart disease using rare association rule mining

Cardiovascular diseases continue to be the leading cause of mortality worldwide, claiming a significant number of lives each year. Despite the advancements in predictive models, including logistic regression, neural networks, and random forests, these techniques often lack transparency and interpretability, limiting their practical application in clinical settings. To address this challenge, this research introduces EPFHD-RARMING, an innovative approach designed to enhance the understanding and predictability of heart disease through the discovery of rare and meaningful patterns. EPFHD-RARMING utilizes rare association rule mining to uncover hidden and unexpected rules that identify critical factors contributing to heart disease. This method is particularly adept at identifying high-risk patterns in individuals who appear healthy but may develop heart disease under certain conditions, thus facilitating early intervention and preventive measures. By integrating these insights with established feature engineering techniques, EPFHD-RARMING enhances its practical utility, enabling medical professionals to proactively manage patient care and tailor interventions to individual risk profiles. This study demonstrates the effectiveness of EPFHD-RARMING in providing a deeper, actionable understanding of the complex dynamics of heart disease. The model’s ability to identify and interpret rare patterns holds significant promise for advancing medical analytics and improving patient outcomes. Moreover, the applicability of EPFHD-RARMING extends beyond the healthcare domain, offering valuable insights in various fields where the discovery of rare patterns is critical, such as finance, marketing, and cybersecurity. This study conducts a comprehensive evaluation, which demonstrates the superior performance of EPFHD-RARMING compared to traditional predictive models in identifying key factors contributing to heart disease, in terms of interestingness, explainability, and comprehensiveness of insights. The results underscore the potential of this innovative approach to revolutionize our understanding and prediction of heart disease, ultimately contributing to more effective and personalized healthcare solutions. This research emphasizes the importance of rare association rule mining in medical analytics and paves the way for future studies to explore and utilize these techniques across diverse domains.

Discussion on Redundant Processing Algorithm of Association Rules Based on Hypergraph in Data Mining

With the rapid advancement of big data, it is becoming a great problem for people to find objective information in the database. The relevance data processing rule for digging the information can be the way. Relevance data processing rule for digging the information is mainly studied in three aspects: data dimension, data abstraction level, and processing variable type. In the aspect of rules, the research mainly focuses on three aspects: active relationships, passive rules, and uncommon relationship rules. Association rules of digging the data can be the most well-employed investigation goal and aim for the data digging. Along with the advancement of the information scale, the time rate of traditional relationship rules exploration of counting ways is too low. How to increase the time rate for the way of counting is the main research content of relevance data processing rules for digging the information. Current relevance data processing rules for digging the information have two limitations: (1) metrics such as support, confidence, and lift rely too much on expert knowledge or complex adjustment processes in value selection; (2) it is often difficult to explain rare association rules. Based on the existing research, this paper proposes a Markov logic network framework model of association rules to address the above shortcomings. The theory of a hypergraph and system is proposed, and the method of a hypergraph in 3D matrix modeling is studied. Aiming at the new characteristics of big data analysis, a new super edge definition method is introduced according to the definition of the system, which greatly enhances the ability to solve problems. In the cluster analysis and calculation of hypergraph, this paper will use the hypergraph segmentation operator hMETIS to carry out the cluster analysis method in order to achieve higher accuracy in cluster analysis and calculation. As for the test of cycle ones, which is in line with the relevance of the hypergraph with clear directions, the thesis will offer a brand-new way to make an analysis and turn the rule of relevance into the hypergraph with clear directions with a new definition of the near linking matrix, and it will change the dealing way from the test of the cycle and more ones into the linking f bricks and circles, which is a new way to explore. This paper uses two datasets of different sizes to conduct rule prediction accuracy experiments on the Markov logic network framework model algorithm of association rules and the traditional association rule algorithm. The results show that compared with the traditional association rule algorithm, the rules obtained by the Markov logic network framework model of association rules have a higher prediction accuracy.

An incremental rare association rule mining approach with a life cycle tree structure considering time-sensitive data

An incremental rare association rule mining approach with a life cycle tree structure considering time-sensitive data

Rare Itemsets Selector with Association Rules for Revenue Analysis by Association Rare Itemset Rule Mining Approach

Background: Knowledge discovering rare itemsets mining, and association rules are significant in a transactional dataset. Be that as it may, rare association rules are now and again more intriguing than frequent association rules since rare rules indicate an unforeseen or obscure association. The utilization of rare itemsets mining with item selector is unavoidable and has turned into an emerging field of research; therefore, this subject has numerous challenges. Objective: To perform the revenue examination of the marketing sector by rare itemsets selector by threshold and time series-based prediction technique. Methods: This paper gives the revenue examination of marketing sector by rare itemsets selector by threshold and time series-based prediction technique. A new algorithm for locating the rare itemsets by Association Rare itemset Rule Mining (ARIRM) to produced rules then utility itemsets discover by the threshold. When the rare patterns are analyzed, the ARIMA model used to anticipate the revenue. Based on the investigation of rare showcasing data with rules of the mining space, this methodology uses a tree structure to learn the rare items. Results: The test results in the "K" transactions with high revenues discovered utilizing the proposed model contrasted with other existing procedures; this forecast procedure is helpful for upcoming transactions. Conclusion: Based on the investigation of rare showcasing data with rules of the mining space, this methodology uses a tree structure to learn the rare items.

FR-Tree: A novel rare association rule for big data problem

In some situations, finding the rare association rule is of higher importance than the frequent itemset. Unique rules represent rare cases, activities, or events in real-world applications. It is essential to extract exceptional critical activity from vast routine data. This paper proposes a new algorithm called FR-Tree to mine the association rules and produce essential rules. This work aims to demonstrate that this algorithm is suitable for extracting rare association rules with high confidence. The proposed algorithm generates, filters, and classifies the all-important rules, either frequent or rare. The rare rules were produced without needing to set an additional threshold. Therefore, the proposed algorithm has an advantage incomparable with the other rare association rule techniques. The generated rules were tested using well-known datasets, and the performance was compared with the other rare association rule techniques. The results proved that our method outperformed the existing rare association rule techniques.

HyIDSVis: hybrid intrusion detection visualization analysis based on rare category and association rules

HyIDSVis: hybrid intrusion detection visualization analysis based on rare category and association rules

FRI-miner: fuzzy rare itemset mining

Data mining is a widely used technology for various real-life applications of data analytics and is important to discover valuable association rules in transaction databases. Interesting itemset mining plays an important role in many real-life applications, such as market, e-commerce, finance, and medical treatment. To date, various data mining algorithms based on frequent patterns have been widely studied, but there are a few algorithms that focus on mining infrequent or rare patterns. In some cases, infrequent or rare itemsets and rare association rules also play an important role in real-life applications. In this paper, we introduce a novel fuzzy-based rare itemset mining algorithm called FRI-Miner, which discovers valuable and interesting fuzzy rare itemsets in a quantitative database by applying fuzzy theory with linguistic meaning. Additionally, FRI-Miner utilizes the fuzzy-list structure to store important information and applies several pruning strategies to reduce the search space. The experimental results show that the proposed FRI-Miner algorithm can discover fewer and more interesting itemsets by considering the quantitative value in reality. Moreover, it significantly outperforms state-of-the-art algorithms in terms of effectiveness (w.r.t. different types of derived patterns) and efficiency (w.r.t. running time and memory usage).

An Efficient Method for Mining Rare Association Rules: A Case Study on Air Pollution

Most pattern mining techniques almost singularly focus on identifying frequent patterns and very less attention has been paid to the generation of rare patterns. However, in several domains, recognizing less frequent but strongly related patterns have greater advantage over the former ones. Identification of compelling and meaningful rare associations among such patterns may proved to be significant for air quality management that has become an indispensable task in today’s world. The rare correlations between air pollutants and other parameters may aid in restricting the air pollution to a manageable level. To this end, efficient and competent rare pattern mining techniques are needed that can generate the complete set of rare patterns, further identifying significant rare association rules among them. Moreover, a notable issue with databases is their continuous update over time due to the addition of new records. The users requirement or behavior may change with the incremental update of databases that makes it difficult to determine a suitable support threshold for the extraction of interesting rare association rules. This paper, presents an efficient rare pattern mining technique to capture the complete set of rare patterns from a real environmental dataset. The proposed approach does not restart the entire mining process upon threshold update and generates the complete set of rare association rules in a single database scan. It can effectively perform incremental mining and also provides flexibility to the user to regulate the value of support threshold for generating the rare patterns. Significant rare association rules representing correlations between air pollutants and other environmental parameters are further extracted from the generated rare patterns to identify the substantial causes of air pollution. Performance analysis shows that the proposed method is more efficient than existing rare pattern mining approaches in providing significant directions to the domain experts for air pollution monitoring.

Discovery of rare association rules in the distribution of lawsuits in the federal justice system of Southern Brazil

Discovery of rare association rules in the distribution of lawsuits in the federal justice system of Southern Brazil

An efficient algorithm for unique class association rule mining

Association rule mining is one of the main means in Knowledge discovery and Machine learning. Such kind of rules present knowledge of interrelations among items in a dataset. Class Association Rules (CARs) are a subset of association rules which are always mined using labeled datasets. Simply, a typical CAR has an itemset that is associated to a class label. Mining CARs is vital for construction of pattern or rule-based classification models and has received recently increasing research interest. In this work, a complete efficient but not exhaustive CAR mining algorithm (UniqAR) is introduced. UniqAR generates always and only 100% accurate CARs which are called unique association rules using two rule search hypothesis of Subsumption and Nonsense to find unique itemsets in order to generate the Unique CARs. Unlike alternatives of CAR mining algorithms, UniqAR mined association rules aren’t based on itemset frequency or item selectivity. It can generate both frequent and rare association rules. No preferences of support, coverage, or item participant in itemsets are required to be provided for the proposed mining process. The main contribution of this work to CARs’ state of the art is describing unique itemsets and class association rules and providing an efficient mining process for them. Unlike the other unique rule mining alternatives in the literature, the proposed novel mining process depends on a complete but not exhaustive search that employs rules inter-relations. UniqAR has been modeled with computational analysis and extended evaluation. It is shown that UniqAR can extract all unique itemsets for unique association mining with no need to setup any user preferences, template or any constraints. Moreover, it describes accurately the effects of different dataset criteria like number of attributes/features, feature values, cases, and class labels on UniqAR unique itemset extraction mining process in an efficient way that avoids a huge number of itemsets/cases comparisons. Results have shown that the proposed UniqAR algorithm is feasible and promising.

Refinement and augmentation for data in micro open learning activities with an evolutionary rule generator

AbstractImproving both the quantity and quality of existing data are placed at the center of research for adaptive micro open learning. To cover this research gap, our work targets on the current scarcity of both data and rules that represent open learning activities. An evolutionary rule generator is constructed, which consists of an outer loop and an inner loop. The outer loop runs a genetic algorithm (GA) to produce association rules that can be effective in the micro open learning scenario from a small amount of available data sources; while the inner loop optimizes generated candidates by taking into account both rare and negative association rules (NARs). These optimized rules are further applied in refining and augmenting data denoting learners’ behaviors in open learning into a low‐dimensional, descriptive and interpretable form. The performance of rule discovery and data processing have been empirically evaluated using genuine open learning data.

Study to Determine Adverse Diseases Pattern using Rare Association Rule Mining

Data mining is a method for finding patterns from repositories that remain hidden, unknown but fascinating. It has resulted in a number of strategies and emphasizes the detection of patterns to identify patterns that occur frequently, seldom and rarely. With their implementations, the work has improved the efficiency of the techniques. Yet typical methods for data mining are limited to databases with static behavior. The first move was to investigate similarities between the common objects through association rules mining. The original motivation for the search for these guidelines was the consumers ' shopping patterns in transaction data for supermarkets. This attempts to classify combinations of items or items that influence the presence likelihood of other items or items in a transaction. The request for rare association rule mining has improved in current years. The identification of unusual data patterns is critical, including medical, financial, or security applications. This survey seeks to give an analysis of rare pattern mining strategies, which in general, comprehensive and constructed. We discuss the issues in the quest for unusual rules using conventional association principles. Because mining rules for rare associations are not well known, special foundations still need to be set up.

A survey of evolutionary computation for association rule mining

Association Rule Mining (ARM) is a significant task for discovering frequent patterns in data mining. It has achieved great success in a plethora of applications such as market basket, computer networks, recommendation systems, and healthcare. In the past few years, evolutionary computation-based ARM has emerged as one of the most popular research areas for addressing the high computation time of traditional ARM. Although numerous papers have been published, there is no comprehensive analysis of existing evolutionary ARM methodologies. In this paper, we review emerging research of evolutionary computation for ARM. We discuss the applications on evolutionary computations for different types of ARM approaches including numerical rules, fuzzy rules, high-utility itemsets, class association rules, and rare association rules. Evolutionary ARM algorithms were classified into four main groups in terms of the evolutionary approach, including evolution-based, swarm intelligence-based, physics-inspired, and hybrid approaches. Furthermore, we discuss the remaining challenges of evolutionary ARM and discuss its applications and future topics.

An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks

The security of energy supplies requires the depletion of potential fault events in power transmission networks. To achieve this, sufficient lead time before the happening of a fault event is indispensable for preparing countermeasures. With this inspiration, this paper establishes the fuzzy inference with rare association rule learning system. This ensemble system is designed for the long-term prediction of the spatiotemporal distribution of such energy security weaknesses, that is, to predict when and where these events are more expected to appear. In this system, merely the environmental features rather than the electrical features are needed as inputs. All the selected input features are divided into discrete and continuous features, and are evaluated separately. For the discrete features, the rare association rule learning model is implemented so that the rarely distributed environmental elements are extracted and diagnosed specifically. The risk indices of each element on the overall reliability are worked out as well. For the continuous features, a hierarchical fuzzy inference system along with the rare association rule learning model is deployed to calculate the corresponding risk indices of all the elements. In the hierarchical fuzzy inference system, the probabilistic fuzzy risks are employed instead of the direct fuzzy risks. Then the relative weights of these two sides are optimized. At last, an empirical case based on a practical transmission network is conducted, and the flexibility and the robustness of the proposed system during real applications can be validated consequently.

Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

Abstract This paper introduces a technique that can efficiently identify symptoms and risk factors for early childhood diseases by using feature reduction, which was developed based on Principal Component Analysis (PCA) method. Previous research using Apriori algorithm for association rule mining only managed to get the frequent item sets, so it could only find the frequent association rules. Other studies used ARIMA algorithm and succeeded in obtaining the rare item sets and the rare association rules. The approach proposed in this study was to obtain all the complete sets including the frequent item sets and rare item sets with feature reduction. A series of experiments with several parameter values were extrapolated to analyze and compare the computing performance and rules produced by Apriori algorithm, ARIMA, and the proposed approach. The experimental results show that the proposed approach could yield more complete rules and better computing performance.

Data-driven approach for spatiotemporal distribution prediction of fault events in power transmission systems

The spatiotemporal distribution of future fault events in a power transmission system assists in operational planning and maintenance scheduling. To this end, this paper proposes an environmental attributes-based framework for the spatiotemporal distribution prediction of potential fault events in the system. In this framework, the distribution of future fault events is predicted via the forecasted information of the environmental attributes rather than the electrical attributes. An extensive investigation covering all environmental attributes including the fault causes is presented, and the underlying fault-attribute relationships are explored. Notably, the rare association rule mining is employed to cope with the rare occurred elements in each environmental attribute through five new significance measurements. Next, to distinguish the diverse influence of each environmental element on the reliability of the whole system, the relative weights are developed. Also, the impact of the latent erroneous predictions of the events caused by one fault cause on the overall prediction performance is assessed via an extended definition of the component importance measurement. Ultimately, the efficiency of the modified significance measurements, the prediction performance in the two test cases, and the impact of each single fault cause are validated by an empirical study. The flexibility and the robustness of this framework in real applications are therefore demonstrated.

Discovery of Rare Association Rules in the Distribution of Lawsuits in the Federal Justice System of Southern Brazil

In the context of data mining, infrequent association rules may be beneficial for analysing rare or extreme cases with very low support values and high confidence. In researching risky situations or allocating specific resources, such rules may have a much greater impact than rules with high support value. The objective of this study is to obtain association rules from the database of lawsuits filed in the Federal Court of Southern Brazil in 2016, including both frequent and rare rules. By finding these rules, especially rare ones, the information collected can assist in the decision-making process, in this case, such as training clerks or establishing specialised courts.

Rare association rule mining from incremental databases

Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database. Although numerous attempts pertaining to rare association rule mining can be found in the literature, there are still certain issues that need utmost attention. The most prominent one among them is the rare association rule mining from incremental databases. The existing rare association rule mining techniques are capable of operating only on static databases, assuming that the entire database to be operated on is available during the outset of the mining process. Inclusion of new records, however, may lead to the generation of some new interesting rules from the current set of data, invalidating the previously extracted significant rare association rules. Executing the entire mining process from scratch for the newly arrived set of data could be a tedious affair. With a view to resolve the issue of incremental rare association rule mining, this study presents a single-pass tree-based approach for extracting rare association rules when new data are inserted into the original database. The proposed approach is capable of generating the complete set of frequent and rare patterns without rescanning the updated database and reconstructing the entire tree structure when new transactions are added to the existent database. Experimental evaluation has been carried out on several benchmark real and synthetic datasets to analyze the efficiency of the proposed approach. Furthermore, to assess its applicability in real-world applications, experimental analysis has been performed on a real geological dataset where earthquake records are incrementally being added on an annual basis. Comparative performance analysis demonstrates the preeminence of proposed approach over existing frequent and rare association rule mining techniques.

Applying mutual information for discretization to support the discovery of rare-unusual association rule in cerebrovascular examination dataset

ABSTRACT In knowledge discovery studies, association rules mining has been extensively studied to discover hidden knowledge and relationships among set of items in a transactional dataset. Most research on association rule mining focuses on discovering frequent patterns based on the most frequent items occurring in the dataset. However, the process of extracting rare rules has received less attention. In medical dataset studies, the discovery of rare association rules (RARs) is more challenging, because it could likely be used to obtain more potentially rare and unusual knowledge for physicians, beside frequent association rules. Hence, the aim of this paper is to discover non-frequent or rare-unusual association rules (RUARs) from a stroke medical dataset to provide potential meaningful knowledge to the user domain. A discretization method needs to be performed as the data preprocessing step before generating rules. To the best of our knowledge, fewer studies have focused on the role of discretization results to support the extraction of a better amount and quality of RUARs, particularly for medical datasets. In addition, the extracted RUARs is expected to provide potential new unusual insights on stroke risk patterns. This paper applies mutual information measure to discretize a stroke examination dataset collected from a medical center in Taiwan. The interval merging method was proposed to simplify the discrete form and enrich the quality of generated rules. Towards the end, rare association rules, with relatively low support, were generated by employing the Apriori-Rare method accordingly. In addition, a filtering process was applied to the content of the rule itemsets to discover the expected set of RUARs for physicians. Furthermore, the extracted RUARs was analyzed based on the relative risk values toward the occurrence of stroke. Results indicated that the mutual information discretization outperformed the traditional discretization methods in terms of how the discretization scheme can support the extraction of RUARs with a better quantity and quality measurements for further analysis purpose in medical point of view. Moreover, the proposed method had a relatively higher number of RUARs. The knowledge of unusual rule patterns from rare association rules might provide potential new and unusual insights for medical pratitioners and increase the awareness of stroke examination results.