Complex and large software-intensive systems are increasingly present in several application domains, including Industry 4.0, connected health, smart cities, and smart agriculture, to mention a few. ...These systems are commonly composed of diverse other systems often developed by different organizations using various technologies and, as a consequence, interoperability among these systems becomes difficult. Many architectural strategies for interoperability have already been proposed; however, selecting adequate strategies is challenging. Additionally, it lacks an overview of such strategies. This work presents TASIS, a typology of architectural strategies for the interoperability of software-intensive systems. We also validated it with 33 practitioners from different countries with an extensive experience in integration projects. This work also offers 12 industry-based association rules that suggest how to combine those strategies to mitigate issues at different interoperability levels. As a result, our typology can serve as a starting point to further aggregate new strategies and, ultimately, supports software architects in designing interoperability-driven architectural solutions.
•It is a typology with architectural strategies and their levels of interoperability.•The typology was created from evidence in literature and validated and refined it.•We offer strategy combinations proven in integration projects, derived from association rules.•Software architects can use it to select architectural strategies for their projects.
Using association rules in classification is a great success which produces high accuracy classifiers. Even so, the principal advantage of the associative classifiers lies in interpretation. However, ...pruning the useless rules among the huge set of the mined rules as well as combining them to build a classifier remains a subject for improvement and further research. In this paper, we introduce a new algorithm to build a classifier based on Regularized Class Association Rules in a categorical data space called RCAR. The characteristic of this algorithm is, therefore, threefold: First, mining an exhaustive set of Class Association Rules (CARs) according to a predefined values of support and confidence thresholds. Second, applying a regularized logistic regression algorithm with Lasso penalty on the rules space to build a model that predicts the conditional probability of the existence of the outcome. Useless rules are pruned thanks to the selective nature of Lasso regularization. Third, organizing and visualizing the CARs which survive the first step of pruning by Lasso regularization using metarules. An optional step of pruning could be undertaken on the basis of the metarules and subject knowledge. Likewise, the empirical results indicate that RCAR gives comparable accuracy against Random Forest and GBM.
Association rules are commonly used to provide decision‐makers with knowledge that helps them to make good decisions. Most of the published proposals mine association rules without paying particular ...attention to temporal information. However, in real‐life applications data usually change over time or presenting different temporal situations. Therefore, the extracted knowledge may not be useful, since we may not know whether the rules are currently applicable or whether they will be applicable in the future. For this reason, in recent years, many methods have been proposed in the literature for mining temporal association rules, which introduce a greater predictive and descriptive power providing an additional degree of interestingness. One of the main problems in this research field is the lack of visibility most works suffer since there is no standard terminology to refer to it, making it difficult to find and compare proposals and studies in the field. This contribution attempts to offer a well‐defined framework that allows researchers both to easily locate the previous proposals and to propose well‐grounded methods in the future. To accomplish both objectives, a two‐level taxonomy is proposed according to whether the time variable is considered to provide order to the data collection and to locate some temporal constraints, or whether it is considered as an attribute within the learning process. Some recent applications, available software tools, and a bibliographical analysis in accordance with the Web of Science are also shown. Finally, some critical considerations and potential further directions are discussed.
This article is categorized under:
Technologies > Association Rules
Algorithmic Development > Association Rules
Overview taxonomy on temporal association rule mining by considering the time variable as an integral or implied component.
The inductive learning of fuzzy rule-based classification systems suffers from exponential growth of the fuzzy rule search space when the number of patterns and/or variables becomes high. This growth ...makes the learning process more difficult and, in most cases, it leads to problems of scalability (in terms of the time and memory consumed) and/or complexity (with respect to the number of rules obtained and the number of variables included in each rule). In this paper, we propose a fuzzy association rule-based classification method for high-dimensional problems, which is based on three stages to obtain an accurate and compact fuzzy rule-based classifier with a low computational cost. This method limits the order of the associations in the association rule extraction and considers the use of subgroup discovery, which is based on an improved weighted relative accuracy measure to preselect the most interesting rules before a genetic postprocessing process for rule selection and parameter tuning. The results that are obtained more than 26 real-world datasets of different sizes and with different numbers of variables demonstrate the effectiveness of the proposed approach.
Most of the algorithms for mining quantitative association rules focus on positive dependencies without paying particular attention to negative dependencies. The latter may be worth taking into ...account, however, as they relate the presence of certain items to the absence of others. The algorithms used to extract such rules usually consider only one evaluation criterion in measuring the quality of generated rules. Recently, some researchers have framed the process of extracting association rules as a multiobjective problem, allowing us to jointly optimize several measures that can present different degrees of trade-off depending on the dataset used. In this paper, we propose MOPNAR, a new multiobjective evolutionary algorithm, in order to mine a reduced set of positive and negative quantitative association rules with low computational cost. To accomplish this, our proposal extends a recent multiobjective evolutionary algorithm based on decomposition to perform an evolutionary learning of the intervals of the attributes and a condition selection for each rule, while introducing an external population and a restarting process to store all the nondominated rules found and to improve the diversity of the rule set obtained. Moreover, this proposal maximizes three objectives-comprehensibility, interestingness, and performance-in order to obtain rules that are interesting, easy to understand, and provide good coverage of the dataset. The effectiveness of the proposed approach is validated over several real-world datasets.
As we rely more and more on machine learning models for real-life decision-making, being able to understand and trust the predictions becomes ever more important. Local explainer models have recently ...been introduced to explain the predictions of complex machine learning models at the instance level. In this paper, we propose Local Rule-based Model Interpretability with k-optimal Associations (LoRMIkA), a novel model-agnostic approach that obtains k-optimal association rules from a neighbourhood of the instance to be explained. Compared with other rule-based approaches in the literature, we argue that the most predictive rules are not necessarily the rules that provide the best explanations. Consequently, the LoRMIkA framework provides a flexible way to obtain predictive and interesting rules. It uses an efficient search algorithm guaranteed to find the k-optimal rules with respect to objectives such as confidence, lift, leverage, coverage, and support. It also provides multiple rules which explain the decision and counterfactual rules, which give indications for potential changes to obtain different outputs for given instances. We compare our approach to other state-of-the-art approaches in local model interpretability on three different datasets and achieve competitive results in terms of local accuracy and interpretability.
Purpose/significance The two major measures to strengthen Chinese industrial infrastructure capacity, including the “Strengthening the Basic Domain Standards System” and the “Strengthening the ...Development of Manufacturing-Oriented Information Technology Services,” were proposed for “Made in China 2025”.Based on this and the actual demand for Chinese aeronautical manufacturing industry to complete product R&D under the new situation of the industrial revolution, this paper try to provide high-quality and efficient knowledge management information services for aviation manufacturing industry in China. Method/process Starting from the current national and industry standards documents in the field of aviation manufacturing in China. This paper analyzed the association rule of standard literature knowledge element in aviation manufacturing from the aspects of explicit association and invisible association, and based on the knowledge element, it established knowledge association network description model of avi
Over the past few decades, fuzzy systems have been widely used in several application fields, thanks to their ability to model complex systems. The design of fuzzy systems has been successfully ...performed by applying evolutionary and, in particular, genetic algorithms, and recently, this approach has been extended by using multiobjective evolutionary algorithms, which can consider multiple conflicting objectives, instead of a single one. The hybridization between multiobjective evolutionary algorithms and fuzzy systems is currently known as multiobjective evolutionary fuzzy systems. This paper presents an overview of multiobjective evolutionary fuzzy systems, describing the main contributions on this field and providing a two-level taxonomy of the existing proposals, in order to outline a well-established framework that could help researchers who work on significant further developments. Finally, some considerations of recent trends and potential research directions are presented.
Early detection of patients with elevated risk of developing diabetes mellitus is critical to the improved prevention and overall clinical management of these patients. We aim to apply association ...rule mining to electronic medical records (EMR) to discover sets of risk factors and their corresponding subpopulations that represent patients at particularly high risk of developing diabetes. Given the high dimensionality of EMRs, association rule mining generates a very large set of rules which we need to summarize for easy clinical use. We reviewed four association rule set summarization techniques and conducted a comparative evaluation to provide guidance regarding their applicability, strengths and weaknesses. We proposed extensions to incorporate risk of diabetes into the process of finding an optimal summary. We evaluated these modified techniques on a real-world prediabetic patient cohort. We found that all four methods produced summaries that described subpopulations at high risk of diabetes with each method having its clear strength. For our purpose, our extension to the Buttom-Up Summarization (BUS) algorithm produced the most suitable summary. The subpopulations identified by this summary covered most high-risk patients, had low overlap and were at very high risk of diabetes.
This paper is the second part of a two-part paper, which is a survey of multiobjective evolutionary algorithms for data mining problems. In Part I , multiobjective evolutionary algorithms used for ...feature selection and classification have been reviewed. In this part, different multiobjective evolutionary algorithms used for clustering, association rule mining, and other data mining tasks are surveyed. Moreover, a general discussion is provided along with scopes for future research in the domain of multiobjective evolutionary algorithms for data mining.
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