Weighted Association Rule Mining Research Articles

A novel software defect prediction approach via weighted classification based on association rule mining

Software defect prediction technology is used to assist software practitioners in effectively allocating test resources and identifying hidden defects in a timely manner. However, the prediction of defect-prone software using association rule mining algorithms is limited because of the unbalanced distribution of defect data. Furthermore, although the existing weighted association rule mining approach considers item strength, the weight calculation still relies on expert experience and lacks fine granularity. We propose a novel software defect prediction approach based on mutual information and correlation coefficient weighted class association rule mining (MCWCAR). The MCWCAR model employs a cost-sensitive strategy and generates frequent itemsets according to three mining objectives while maintaining the original item distribution: defective class rules, non-defective class rules, and feature association relationships. During the weighted frequent itemset mining process, it combines feature selection and itemset screening to determine the appropriate feature combination through mutual information weighted support. Meanwhile, the correlation coefficient is applied to accurately depict the correlation between feature items and defect classes, serving as the weight to mine class association rules. Additionally, to ensure that interestingness measures have asymmetry and effectively represent negative associations under the condition of class imbalance, the addedvalue is adopted in the filtering association rules. We conducted experiments on 27 open-source datasets and evaluated the performance differences between MCWCAR and state-of-the-art baseline classifiers. Experimental results demonstrate that the proposed algorithm significantly outperforms other baselines in terms of Balance, Gmean, MCC, and F-measure.

Characteristics analysis of intercontinental sea accidents using weighted association rule mining: Evidence from the Mediterranean Sea and Black Sea

With the continuous growth of international trade and the number of ships, the pressure on marine safety is increasing. To strengthen the efficiency and pertinence of accident prevention and control measures, in-depth research on the characteristics of marine traffic accidents is of great significance. This study uses a Weighted Association Rule Mining (WARM) approach to investigate the association between marine traffic accidents characteristics. The Mediterranean Sea and Black Sea play a crucial role in global maritime transportation. The marine traffic accidents in the Mediterranean Sea and Black Sea region from 2006 to 2020 are collected from the Lloyd's List Intelligence (LLI) database. Then, WARM is applied to obtain the characteristics of marine traffic accidents. The findings show that Flag of Convenience (FOC) vessels in the Mediterranean have a lower accident rate than non-FOC vessels do. Moreover, the results reveal a strong relationship between accidents and the age, gross tonnage, and ship type. Older general cargo ships with a gross tonnage between 500 and 3000 tons are more prone to accidents. This research provides insights for authorities to develop targeted measures for preventing specific types of accidents and enhancing marine safety.

Robust Anomaly Clue Localization of Multi-Dimensional Derived Measure for Online Video Services

Anomaly clue localization of multi-dimensional derived measure is vitally important for the reliability of online video services. In this paper, we propose RobustSpot, an end-to-end framework for localizing the clues to anomalous multi-dimensional derived measures. RobustSpot integrates two novel indicators, i.e., “Anomaly Degree” and “Contribution Ability”, with a simple yet effective method, weighted association rule mining (WARM), to automatically mine the hidden relationships across data dimensions for localizing the most likely clues to the root cause. Using 135 real-world cases collected from a top-tier global online video service provider <inline-formula><tex-math notation="LaTeX">$H$</tex-math></inline-formula> with 170+ million monthly active users, we demonstrate that RobustSpot achieves high accuracy (Top-5 accuracy of 98%), significantly outperforming state-of-the-art methods. The average localization time of RobustSpot is 1.83s, which is satisfying in our scenario. We have open-sourced the implementation of RobustSpot as well as the data used in the evaluation experiments.

Fuzzy weighted Bayesian belief network: a medical knowledge-driven Bayesian model using fuzzy weighted rules.

In this current work, Weighted Bayesian Association rules using the Fuzzy set theory are proposed with the new concept of Fuzzy Weighted Bayesian Association Rules to design and develop a Clinical Decision Support System on the Bayesian Belief Network, which is an appropriate area to work in Clinical Domain as it has a higher degree of unpredictability and causality. Weighted Bayesian Association rules to construct a Bayesian network are already proposed. A "Sharp boundary" issue related to quantitative attribute domains may cause erroneous predictions in medicine and treatment in the medical environment. So to eradicate sharp boundary problems in the medical field, the fuzzy theory is applied in attributes to deal with real-life situations. A new algorithm is designed and implemented in this paper to set up a new Bayesian belief network using the concept of Fuzzy Weighted Association rule mining under the Predictive Modeling paradigm named Fuzzy weighted Bayesian belief network using numerous clinical datasets with outshone results.

Weighted Bayesian Belief Network: A Computational Intelligence Approach for Predictive Modeling in Clinical Datasets.

There are growing concerns about the mortality due to Breast cancer many of which often result from delayed detection and treatment. So an effective computational approach is needed to develop a predictive model which will help patients and physicians to manage the situation timely. This study presented a Weighted Bayesian Belief Network (WBBN) modeling for breast cancer prediction using the UCI breast cancer dataset. New automated ranking method was used to assign proper weights to attribute value pair based on their impact on causing the disease. Association between attributes was generated using weighted association rule mining between two attributes, multiattributes, and with class labels to generate rules. Weighted Bayesian confidence and weighted Bayesian lift measures were used to produce strong rules to build the model. To build WBBN, the Open Markov tool was used for structure and parametric learning using generated strong rules. The model was trained using 70% records and tested on 30% records with a threshold value of minimum support = 36% and confidence = 70% which produced results with an accuracy of 97.18%. Experimental results show that WBBN achieved better results in most cases compared to other predictive models. The study would contribute to the fight against breast cancer and the quality of treatment.

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.

An Improved Recognition Method of Weighted Rules and Its Application in Recommendation Algorithm

The method proposed in this paper mainly uses the difference of the importance of items in the database to improve the problem of mining a large number of redundant and useless rules by traditional association rules mining algorithm. The method has made great improvement in three aspects of association rule design, which are effective length recognition of recommendation rule, weighted association rule mining combined with frequency analysis and time constraint application. The weighted temporal association rules mined by the effective frequency length weighted association rules mining algorithm can improve the accuracy of recommendation, and the accuracy of recommendation prediction is increased from 62% to 69%. The purpose of this algorithm is to mine hidden high value rules, and optimize the algorithm considering the time complexity of algorithm execution while ensuring the accuracy of the algorithm.

Multidrug resistance analysis method for pathogens of cow mastitis based on weighted-association rule mining and similarity comparison

Mastitis is one of the most common diseases and causes the greatest economic loss in dairy farming. Antibiotics are the most effective drugs to prevent and treat bacterial infection of mastitis. However, yet the growing problem of drug resistance, especially multidrug resistance (MDR), poses a great threat to disease control. To understand the MDR rules in bacteria of cow mastitis from national level, the main bacteria from cows with mastitis in large-scale farms were isolated and identified in China, and then drug sensitivity tests were conducted to establish a drug resistance data set. Aiming at the problem of numerous and disordered drug resistance data and lack of extensive correlations, a weighted Apriori association rule mining algorithm in conjunction with the bacterial drug resistance prevalence is proposed. We analyzed the associations between different antibiotics of key bacteria, extracted and visualized the key trends of high resistance prevalence and frequent occurrence, and discovered MDR patterns. Finally, a similarity comparison method based on Euclidean measurement was proposed to compare the relative MDR rules of different bacteria from the overall level with support, confidence, and promotion as characteristic parameters. The drug resistance data set showed that staphylococcus were the main bacteria isolated from dairy cow mastitis in China. Then based on the association rule algorithm, the important rules between different antibiotics resistance in this dataset were identified. In addition, the MDR patterns of different bacteria were visualized and analyzed by using the chord diagram. The results showed the bacteria are highly resistant to penicillin, gentamicin, and ampicillin, and most other antibiotics were linked with these three antibiotics. Finally, the high correlations and main rules in different bacteria were confirmed by a similarity comparison method. The assessment model and conclusions of this study are potentially valuable for assessing the evolution of MDR patterns, providing a scientific basis for relevant authorities to guide the rational use of antibiotics in the farming industry.

Exploiting weighted association rule mining for indicating synergic formation tactics in soccer teams

SummaryManagers make decisions on team tactics, formations, and player selection based on their own experiences. The managers have limitations in understanding the team's situation and sometimes they can think wrong. The purpose of this study is to make decisions on player selection and tactical formation according to the level of the opponent based on the data, not on the intuition of the manager. In our previous study, the Boruta algorithm was used to extract important features from 69 features in soccer player data by position. The detailed roles of each position were defined by using K‐means algorithm. For example, the detailed roles of each position were defined as Mezzala, Shadow Striker, Deep‐lying playmaker, and so on. That is, forward positions are classified as Target Man (TM) and Shadow Striker (SS). TM is a high‐goal, high‐competitive forward, and SS is a high‐dribble, high‐pass forward. In this study, we analyze a clustering dataset and the game appearance dataset. The game appearance dataset are divided into CL (Champions league Level), EL (Europa league Level), ML (Middle Level), and RL (Relegation Level). Association rule mining algorithm analyzes the synergy between positions, and selects a position with high synergy. Weighted association rule mining algorithm establishes player selection and tactical formation with the weight, which is the player's rating data. Finally, using the obtained results, we visualize the synergy between positions, tactical formation, and player characteristics depending on the level of the opponent.

Integrating rough set theory with customer satisfaction to construct a novel approach for mining product design rules

When users choose a product, they consider the emotional experience triggered by the product form. In view of the fact that traditional kansei engineering can not effectively reflect the complex and changeable psychological factors of users, and it has not explored the complex relationship between customer satisfaction and perceptual demand characteristics. To address this problem, some uncertainty techniques including rough sets and fuzzy sets are applied to capture more accurate emotion knowledge. Therefore, this research proposes an integrated evaluation gird method (EGM), rough set theory (RST), continuous fuzzy kano model (CFKM), fuzzy weighted association rule mining method to extract the significant relationship between user needs and product morphological features. The EGM is applied to analyze the attractive factor of morphological characteristics of the product, and then the demand items with the highest satisfaction are analyzed through CFKM. The semantic difference method is combined to construct a decision table, and through attribute reduction and importance calculation to obtain the weight of the core product design items. In order to explore the non-linear relationship between design elements and kansei images, the fuzzy weighted association rule mining method was applied to obtain the set of frequent fuzzy weighted association rules based on evidence theory’s reliability indices of minimum support and confidence so as to realize user demand-driven product design. Taking the design of electric bicycle as an example, the experiment results show that the proposed method can help companies or designers develop products to generate good solutions for customer need.

Weighted Association Rule Mining Over Unweighted Databases Using Inter-Item Link Based Automated Weighting Scheme

Weighted association rule mining is an effective approach in discovering hidden relationships among the important items in a transactional database. Weight of an item reflects its importance in the database. However, most of the traditional methods are suitable for weighted item transaction databases (WITDs) where the item weights are available. In case of the unweighted item transaction databases (UWITDs), these methods remain ineffective. Item weights are not available in the UWITDs, and hence, the task of weight assignment has become one of the prime issues in this respect. This paper presents an automated weight assignment scheme for the items in an UWITD using the inter-item links. Unlike the existing approaches, the proposed scheme considers the indirect links in addition to the direct links among the items. Indirect links adjust the weights of the items, which in later help in mining large itemsets with low supports. We propose a link-based weighted association rule mining approach over the UWITD. The proposed approach includes two new objective measures such as linkage weighted support and linkage weighted confidence for mining the frequent weighted itemsets (FWIs) and the weighted association rules (WARs), respectively. The comprehensive experiments on both of the synthetic and real-world datasets show the effectiveness of the proposed approach in terms of number of FWIs and WARs, runtime, memory usage, weight distribution, scalability and dissociation.

Development of Multimorbidity Over Time: An Analysis of Belgium Primary Care Data Using Markov Chains and Weighted Association Rule Mining.

BackgroundThe prevalence of multimorbidity is increasing in recent years, and patients with multimorbidity often have a decrease in quality of life and require more health care. The aim of this study was to explore the evolution of multimorbidity taking the sequence of diseases into consideration.MethodsWe used a Belgian database collected by extracting coded parameters and more than 100 chronic conditions from the Electronic Health Records of general practitioners to study patients older than 40 years with multiple diagnoses between 1991 and 2015 (N = 65 939). We applied Markov chains to estimate the probability of developing another condition in the next state after a diagnosis. The results of Weighted Association Rule Mining (WARM) allow us to show strong associations among multiple conditions.ResultsAbout 66.9% of the selected patients had multimorbidity. Conditions with high prevalence, such as hypertension and depressive disorder, were likely to occur after the diagnosis of most conditions. Patterns in several disease groups were apparent based on the results of both Markov chain and WARM, such as musculoskeletal diseases and psychological diseases. Psychological diseases were frequently followed by irritable bowel syndrome.ConclusionsOur study used Markov chains and WARM for the first time to provide a comprehensive view of the relations among 103 chronic conditions, taking sequential chronology into consideration. Some strong associations among specific conditions were detected and the results were consistent with current knowledge in literature, meaning the approaches were valid to be used on larger data sets, such as National Health care Systems or private insurers.

Root cause analysis approach based on reverse cascading decomposition in QFD and fuzzy weight ARM for quality accidents

Quality accidents (QAs) of high frequencies in various fields have caused large economic and reputational losses to manufacturers, and identification of the root causes of vicious QAs is a top priority and a major challenge for manufacturers. Especially in the era of big data, the large number of data could be collected from the product life cycle easily, these high-dimensional big data always bear so many un-correlation noise information, which has caused serious problem. The accurate and heuristic root cause analysis for QAs is an important and challenging task in exploring this mechanism due to the fuzzy and vague nature of the collected big quality data. Thus, in this study, a heuristic root cause identification solution based on the fuzzy weighted association rule mining (FWARM) for QAs is proposed. First, the formation mechanism of QAs and big quality accident data is expounded, and a big data driven root cause analysis framework of QAs is presented with the aid of reverse cascading decomposition in Quality Function Deployment (QFD). Second, principal component analysis (PCA) is adopted to eliminate redundancy and reduce data dimension of original process feature parameters from raw data in low-dimensional space so that the key variables as the potential root cause candidates can be extracted. Third, considering the fuzzy mechanism and vague nature of big data, a heuristic root cause identification approach based on FWARM is established, and the weight of nodes on the accident-relevance tree is computed by fuzzy weight coefficient. Finally, the proposed approach is verified with a case study of a quality accident analysis of a washing machine. Results shows that the proposed approach is conducive to heuristically identify the root causes of QAs in the context of big data.

Using the fuzzy weighted association rule mining approach to develop a customer satisfaction product form

Using the fuzzy weighted association rule mining approach to develop a customer satisfaction product form

Medical consumable usage control based on Canopy_K-means clustering and WARM

Medical consumable usage is ineluctable in treatment process. High consumable cost not only brings pressure to the patients and their families, but also reduces the performance of hospital operation management. Therefore, precise medical consumable usage management is very important to the hospital. Large amounts of data accumulated over the years in hospital provide a resource for pattern and rule discovery. A medical consumable usage control method based on Canopy_K-means and Weighted Association Rules Mining (WARM) is proposed in this paper. Firstly, Canopy algorithm is used to get rough clusters; Secondly, K-means algorithm is used to get accurate clusters; Thirdly, ARM and WARM are used to discover rules between disease and consumable among a cluster; In the Fourth, the consumable usage control method in daily requisition has been designed. Half-year data from an A-level hospital in Shanghai have been studied, the results show that WARM can help to find rules between disease and consumable, and the control method based on WARM is feasible to apply.

Early warning system for spatiotemporal prediction of fault events in a power transmission system

Predictions of the spatiotemporal distribution of fault events help the management planning and the maintenance scheduling in power transmission systems. To this end, this study proposes an early warning system based on the exploration of cause-attribute relationships. A comprehensive assessment of all the fault causes is conducted by incorporating entire available environmental attributes in the investigated system as inputs. To cope with the rarely occurred fault causes and environmental elements, a procedure of the weighted association rule mining is established. Then, a risk evaluation model is employed in evaluating the prediction performance and the risks of possible false predictions. Three requirements for the uncertainty are taken into account when this framework is applied in real use: technical reliability, internal reliability, and factitious effectiveness. An empirical study is conducted in a real power transmission system to verify the feasibility of the proposed framework, and the results prove that a flexible and robust prediction can be generated consequently.

Recent and Frequent Informative Pages from Web Logs by Weighted Association Rule Mining

Recent and Frequent Informative Pages from Web Logs by Weighted Association Rule Mining

A novel approach for disease comorbidity prediction using weighted association rule mining

Disease comorbidity prediction has gained the attention of many researchers during the past years. Bulk creation of clinical data in the form of electronic health records (EHRs) and biological data opened the door to explore disease associations and comorbidity patterns. This led to the development of analytical tools for the detection of disease comorbidities and analysis of their causal genetic source. Comorbidity prediction using statistical analyis, data mining and network analysis have made significant contributions in medical field. Combining multiscalar data proved to have enhanced performance in disease comorbidity prediction techniques. Here we present a novel approach based on weighted association rule mining for predicting disease comorbidities using clinical data and molecular data. Results demonstrated that the system outperformed existing systems in disease comorbidity prediction.

An Improved Mechanism for User Profile Generation Using Case-Based Reasoning and Weighted Association Rule Mining

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Applying data mining and machine learning techniques for sentiment shifter identification

Sentiment shifters, as a set of words and expressions that can affect text polarity, play a fundamental role in opinion mining. However, the limited ability of current automated opinion mining systems in handling shifters is a major challenge. This paper presents three novel and efficient methods for identifying sentiment shifters in reviews in order to improve the overall accuracy of opinion mining systems: two data mining based algorithms and a machine learning based algorithm. The data mining algorithms do not need shifter tagged datasets. They use weighted association rule mining (WARM) for finding frequent patterns representing sentiment shifters from a domain-specific and a general corpus. These patterns include different kinds of shifter words such as shifter verbs and quantifiers and are able to handle both local and long-distance shifters. The items in WARM for the two designed methods are in the form of dependency relations and SRL arguments of sentences, respectively. Secondly, we implemented a supervised machine learning system based on semantic features of sentences for shifter identification and polarity classification. This method obviously needs shifter tagged dataset for shifter identification. We tested our proposed algorithms on polarity classification task for 2 domains: a specific domain (drug reviews) and a general domain. Experiments demonstrate that (1) the extracted shifters improve the performance of the polarity classification, (2) the proposed data mining methods outperform other implemented methods in shifter identification, and (3) the proposed semantic based machine learning method has the best efficiency among all implemented methods in polarity classification.