Rule Discovery Research Articles

New Energy Station Monitoring Equipment Rule Verification Model Based on Data Envelopment Analysis

According to the functional and performance requirements of the new energy station monitoring equipment, an operating rule verification model based on data envelopment analysis is proposed, and the online automatic discovery of equipment and safe operation rules for key links are designed. The equipment adopts data envelopment analysis method to read real-time data of new energy station monitoring equipment, optimizes the automatic discovery function of key sections, and tests the operating parameters of wind turbines and wind farms online through remote testers, formulates monitoring rules, and forms data requirements, Use algorithms to acquire and process data, and build models. Realize the discrete processing and rule mining of key links and safety operating procedures to ensure the safe operation of new energy station monitoring equipment. Finally, the experimental results show that the equipment can quickly and automatically generate new energy station monitoring equipment safe operation knowledge and improve new energy Safety of station monitoring equipment.

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

A Novel Machine Learning Approach to Classify and Detect Atrial Fibrillation Using Optimized Implantable Electrocardiogram Sensor

There are some constraints such as external electrodes, a failure to capture most paroxysmal atrial fibrillation (AFib), low power transfer efficiency (PTE) for 24/7 charging technology, a short period of monitoring, and automatic detection of AFib in conventional electrocardiogram (ECG) sensors. To overcome these constraints, an implantable ECG sensor with a 2-coil inductive link with maximum power transfer efficiency (PTE) is designed to continuously monitor patients and efficiently detect AFib using global covering rule discovery and the minimum description length (MDL) algorithm. Among different combinations of ECG coils, the square spiral-square spiral coil demonstrates the maximum PTE, 56.23%, at the resonant frequency of 13.56 MHz and it is used in the implantable ECG sensor. The QRS complex from ECG signals of twenty-nine AFib patients is detected using different operation methods (DOM). The MDL algorithm is used to group 12 features of heart rate variability (HRV) parameters. The global covering rule discovery is proposed as a novel classification technique of AFib in ECG data. The average classification accuracy was 96.67 ± 7.03, and then the average recall, precision, F1-measures, and an average number of generated rules were 97.08 ± 6.23, 97.08 ± 6.23, 96.57 ± 7.23, and 7.9 ± 0.32, respectively. We found that the NN50, pNN50, and LF parameters can distinguish the AFib patient better than a healthy one. Among these parameters, pNN50 showed that it is greater than 34.75 in 41.38% of patients. The optimized implantable ECG sensor with a maximum PTE of 56.23% along with novel AFib detection and classification methods is suitable for its implementation in future implantable ECG sensors.

Elementary Analysis of the Content and Characteristics of College Students' Consciousness of Rules under the “COVID-19”

The condition of the “COVID-19” puts forward new requirements for the rule consciousness of Chinese citizens. As the hope for the future of the motherland and the nation, college students are excellent groups with better political quality, higher moral quality and good behavior habits. As the builders and successors of socialism with Chinese characteristics, college students' rule consciousness includes rule supremacy consciousness, rule discovery consciousness, rule identification consciousness and rule compliance consciousness, under the epidemic situation, college students' consciousness of rules also has the new characteristics of high cognition and recognition of rules, strong willpower to abide by rules, and actively abide by and practice all kinds of rules.

Temporal association rules discovery algorithm based on improved index tree

Abstract With the rapid increase of information generated from all kinds of sources, temporal big data mining in business area has been paid more and more attention recently. A novel data mining algorithm for mining temporal association is proposed. Mining temporal association can not only provide better predictability for customer behaviour but also help organisations with better strategies and marketing decisions. To compare the proposed algorithm, two methods to mine temporal association are presented. One is improved based on a traditional mining algorithm, Apriori. The other is based on an Index-Tree. Moreover, the proposed method is extended to mine temporal association in multi-dimensional space. The experimental results show that the Index-Tree method outperforms the Apriori-modified method in all cases.

Data mining approach to model bus crash severity in Australia

Introduction: Buses are different vehicles in terms of dimensions, maneuverability, and driver's vision. Although bus traveling is a safe mode to travel, the number of annual bus crashes cannot be neglected. Moreover, limited studies have been conducted on the bus involved in fatal crashes. Therefore, identification of the contributing factors in the bus involved fatal crashes can reduce the risk of fatality. Method: Data set of bus involved crashes in the State of Victoria, Australia was analyzed over the period of 2006–2019. Clustering of crash data was accomplished by dividing them into homogeneous categories, and by implementing association rules discovery on the clusters, the factors affecting fatality in bus involved crashes were extracted. Results: Clustering results show bus crashes with all vehicles except motor vehicles and weekend crashes have a high rate of fatality. According to the association rule discovery findings, the factors that increase the risk of bus crashes with non-motor vehicles are: old bus driver, collision with pedestrians at signalized intersections, and the presence of vulnerable road users. Likewise, factors that increase the risk of fatality in bus involved crashes on weekends are: darkness of roads in high-speed zones, pedestrian presence at highways, bus crashes with passenger car by a female bus driver, and the occurrence of multi-vehicle crashes in high-speed zones. Practical Applications: The study provides a sequential pattern of factors, named rules that lead to fatality in bus involved crashes. By eliminating or improving one or all of the factors involved in rules, fatal bus crashes may be prevented. The recommendations to reduce fatality in bus crashes are: observing safe distances with the buses, using road safety campaigns to reduce pedestrians’ distracted behavior, improving the lighting conditions, implementing speed bumps and rumble strips in high-speed zones, installing pedestrian detection systems on buses and setting special bus lanes in crowded areas.

Comparison of Different Data Mining Methods to Determine Disease Progression in Dissimilar Groups of Parkinson’s Patients

Parkinson’s disease (PD) is the second after Alzheimer’s most popular neurodegenerative disease (ND). Cures for both NDs are currently unavailable. OBJECTIVE: The purpose of our study was to predict the results of different PD patients’ treatments in order to find an optimal one. METHODS: We have compared rough sets (RS) and others, in short, machine learning (ML) models to describe and predict disease progression expressed as UPDRS values (Unified Parkinson’s Disease Rating Scale) in three groups of Parkinson’s patients: 23 BMT (Best Medical Treatment) patients on medication; 24 DBS patients on medication and on DBS therapy (Deep Brain Stimulation) after surgery performed during our study; and 15 POP (Postoperative) patients who had had surgery earlier (before the beginning of our research). Every PD patient had three visits approximately every six months. The first visit for DBS patients was before surgery. On the basis of the following condition attributes: disease duration, saccadic eye movement parameters, and neuropsychological tests: PDQ39 (Parkinson’s Disease Questionnaire - disease-specific health-related quality-of-life questionnaire), and Epworth Sleepiness Scale tests we have estimated UPDRS changes (as the decision attribute). RESULTS: By means of RS rules obtained for the first visit of BMT/DBS/POP patients, we have predicted UPDRS values in the following year (two visits) with global accuracy of 70% for both BMT visits; 56% for DBS, and 67%, 79% for POP second and third visits. The accuracy obtained by ML models was generally in the same range, but it was calculated separately for different sessions (MedOFF/MedON). We have used RS rules obtained in BMT patients to predict UPDRS of DBS patients; for the first session DBSW1: global accuracy was 64%, for the second DBSW2: 85% and the third DBSW3: 74% but only for DBS patients during stimulation-ON. ML models gave better accuracy for DBSW1/W2 session S1(MedOFF): 88%, but inferior results for session S3 (MedON): 58% and 54%. Both RS and ML could not predict UPDRS in DBS patients during stimulation-OFF visits because of differences in UPDRS. By using RS rules from BMT or DBS patients we could not predict UPDRS of POP group, but with certain limitations (only for MedON), we derived such predictions for the POP group from results of DBS patients by using ML models (60%). SIGNIFICANCE: Thanks to our RS and ML methods, we were able to predict Parkinson’s disease (PD) progression in dissimilar groups of patients with different treatments. It might lead, in the future, to the discovery of universal rules of PD progression and optimise the treatment.

Evaluating pattern restrictions for associative classifiers

Associative classification is a pattern recognition approach that integrates classification and association rule discovery to build accurate classification models. These models are formed by a collection of contrast patterns that fulfill some restrictions. In this paper, we introduce an experimental comparison of the impact of using different restrictions in the classification accuracy. To the best of our knowledge, this is the first time that such analysis is performed, deriving some interesting findings about how restrictions impact on the classification results. Contrasting these results with previously published papers, we found that their conclusions could be unintentionally biased by the restrictions they used. We found, for example, that the jumping restriction could severely damage the pattern quality in the presence of dataset noise. We also found that the minimal support restriction has a different effect in the accuracy of two associative classifiers, therefore deciding which one is the best depends on the support value. This paper opens some interesting lines of research, mainly in the creation of new restrictions and new pattern types by joining different restrictions.

Interpretable Rule Discovery Through Bilevel Optimization of Split-Rules of Nonlinear Decision Trees for Classification Problems.

For supervised classification problems involving design, control, and other practical purposes, users are not only interested in finding a highly accurate classifier but they also demand that the obtained classifier be easily interpretable. While the definition of interpretability of a classifier can vary from case to case, here, by a humanly interpretable classifier, we restrict it to be expressed in simplistic mathematical terms. As a novel approach, we represent a classifier as an assembly of simple mathematical rules using a nonlinear decision tree (NLDT). Each conditional (nonterminal) node of the tree represents a nonlinear mathematical rule (split-rule) involving features in order to partition the dataset in the given conditional node into two nonoverlapping subsets. This partitioning is intended to minimize the impurity of the resulting child nodes. By restricting the structure of the split-rule at each conditional node and depth of the decision tree, the interpretability of the classifier is ensured. The nonlinear split-rule at a given conditional node is obtained using an evolutionary bilevel optimization algorithm, in which while the upper level focuses on arriving at an interpretable structure of the split-rule, the lower level achieves the most appropriate weights (coefficients) of individual constituents of the rule to minimize the net impurity of two resulting child nodes. The performance of the proposed algorithm is demonstrated on a number of controlled test problems, existing benchmark problems, and industrial problems. Results on 2-500 feature problems are encouraging and open up further scopes of applying the proposed approach to more challenging and complex classification tasks.

Tort Actions based on Child Sexual Abuse and Statue of Limitation Focus on Discovery Rule

Tort Actions based on Child Sexual Abuse and Statue of Limitation Focus on Discovery Rule

Data mining model for multimedia financial time series using information entropy

At present, KDD research covers many aspects, and has achieved good results in the discovery of time series rules, association rules, classification rules and clustering rules. KDD has also been widely used in practical work such as OLAP and DW. Also, with the rapid development of network technology, KDD research based on WEB has been paid more and more attention. The main research content of this paper is to analyze and mine the time series data, obtain the inherent regularity, and use it in the application of financial time series transactions. In the financial field, there is a lot of data. Because of the huge amount of data, it is difficult for traditional processing methods to find the knowledge contained in it. New knowledge and new technology are urgently needed to solve this problem. The application of KDD technology in the financial field mainly focuses on customer relationship analysis and management, and the mining of transaction data is rare. The actual work requires a tool to analyze the transaction data and find its inherent regularity, to judge the nature and development trend of the transaction. Therefore, this paper studies the application of KDD in financial time series data mining, explores an appropriate pattern mining method, and designs an experimental system which includes mining trading patterns, analyzing the nature of transactions and predicting the development trend of transactions, to promote the application of KDD in the financial field.

A knowledge discovery and visualisation method for unearthing emotional states from physiological data

In this paper we propose a knowledge discovery and visualisation method for unearthing emotional states from physiological data typically available from wearable devices. In addition we investigate the viability of using a limited set of wearable sensors to extract decision tree rules which are representative of physiological changes taking place during emotional changes. Our method utilised a fusion of pre-processing and classification techniques using decision trees to discover logic rules relating to the valence and arousal emotional dimensions. This approach normalised the signal data in a manner that enabled accurate classification and generated logic rules for knowledge discovery. Furthermore, the use of three target classes for the emotional dimensions was effective at denoising the data and further enhancing classification and useful rule extraction. There are three key contributions in this work, firstly an exploration and validation of our knowledge discovery methodology, secondly successful extraction of high accuracy rules derived from physiological data and thirdly knowledge discovery and visualisation of relationships within-participant physiological data that can be inferred relating to emotions. Additionally, this work may be utilised in areas such as the medical sciences where interpretable rules are required for knowledge discovery.

Relational rule discovery in complex discrimination learning.

Failure to learn and generalize abstract relational rules has critical implications for education. In this study, we aimed to determine which training conditions facilitate relational transfer in a relatively simple (patterning) discrimination versus a relatively complex (biconditional) discrimination. The amount of training participants received had little influence on rates of relational transfer. Instead, trial-sequencing of the training contingencies influenced relational transfer in different ways depending on the complexity of the discrimination. Clustering instances of relational rules together during training improved transfer of both simpler patterning and more difficult biconditional rules, regardless of individual differences in cognitive reflection. However, blocking all trials of the same type together improved rule transfer only for biconditional discriminations. Individual differences in cognitive reflection were also more predictive of relational rule use under suboptimal training conditions. The results highlight the need for comprehensive accounts of relational learning to consider how learning conditions and individual differences affect the likelihood of engaging in learning relational structures. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Transfusion de sang total : quelle place dans la réanimation du choc hémorragique traumatique ?

La transfusion sanguine s’est développée et a progressé au rythme des conflits militaires. La découverte des groupes sanguins et des règles de compatibilité croisée a permis d’identifier des « donneurs universels ». Le fractionnement du sang total et le stockage différencié de ses différents composants ont permis d’améliorer la sécurité transfusionnelle et la conservation des produits sanguins. 
 Alors que la majorité des décès évitables en traumatologie sont liés à des hémorragies massives, les engagements militaires récents ont mis en évidence l’intérêt d’une réanimation transfusionnelle précoce et ciblée contre la coagulopathie. À la phase aiguë, globules rouges, facteurs de coagulation et plaquettes sont pour cette raison administrés selon une stratégie de ratios transfusionnels. Cette stratégie se heurte à des difficultés logistiques liées aux différentes conditions de conservation de ces produits, à leur délai de délivrance, et à la charge de travail que leur transfusion impose dans le contexte de l’urgence.
 Dans des environnements contraints, le prélèvement et la transfusion de sang total frais est resté une technique de recours qui a montré son intérêt clinique et logistique. Plusieurs équipes civiles et militaires ont démontré la possibilité de conserver à 4° C puis de transfuser du sang total de groupe O faiblement titré en hémolysine, afin d’obtenir une thérapeutique de réanimation transfusionnelle universelle et disponible sans délai.

Fluid reasoning is equivalent to relation processing

Fluid reasoning (Gf)—the ability to reason abstractly—is typically measured using nonverbal inductive reasoning tests involving the discovery and application of complex rules. We tested whether Gf, as measured by such traditional assessments, can be equivalent to relation processing (a much simpler process of validating whether perceptually available stimuli satisfy the arguments of a single predefined relation—or not). Confirmatory factor analysis showed that the factor capturing variance shared by three relation processing tasks was statistically equivalent to the Gf factor loaded by three hallmark fluid reasoning tests. Moreover, the two factors shared most of their residual variance that could not be explained by working memory. The results imply that many complex operations typically associated with the Gf construct, such as rule discovery, rule integration, and drawing conclusions, may not be essential for Gf. Instead, fluid reasoning ability may be fully reflected in a much simpler ability to effectively validate single, predefined relations.

ABC-RuleMiner: User behavioral rule-based machine learning method for context-aware intelligent services

This paper formulates the problem of a rule-based machine learning method to discover the behavioral rules of individual smartphone users to provide context-aware intelligent services. Smartphones nowadays are considered as one of the most important Internet-of-Things (IoT) devices for providing various context-aware personalized services. These devices can record individuals' contextual data - for example, temporal, spatial, or social contexts, and their daily behavioral activity records. Association rule mining (ARM) is the most popular rule-based machine learning method for discovering rules for a particular constraint preference utilizing a given dataset. However, it generates numerous uninteresting contextual associations which lead to generate huge number of redundant rules that become useless in making context-aware decisions. This redundant generation makes not only the rule-set unnecessarily large but also makes the context-aware decision making process more complex and ineffective. To minimize these issues, in this paper, we propose a rule-based machine learning method “ABC-RuleMiner” that effectively identifies the redundancy in associations, and discovers a set of non-redundant behavioral rules(IF-THEN) for individual users by taking into account the precedence of relevant contexts. Our experiments on individuals’ contextual smartphone datasets show that this rule discovery approach is more effective while comparing with traditional rule-based methods.

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.

Computational discovery and modeling of novel gene expression rules encoded in the mRNA.

The transcript is populated with numerous overlapping codes that regulate all steps of gene expression. Deciphering these codes is very challenging due to the large number of variables involved, the non-modular nature of the codes, biases and limitations in current experimental approaches, our limited knowledge in gene expression regulation across the tree of life, and other factors. In recent years, it has been shown that computational modeling and algorithms can significantly accelerate the discovery of novel gene expression codes. Here, we briefly summarize the latest developments and different approaches in the field.

Automated Discovery of Local Rules for Desired Collective-Level Behavior Through Reinforcement Learning

Complex global behavior patterns can emerge from very simple local interactions between many agents. However, no local interaction rules have been identified that generate some patterns observed in nature, for example the rotating balls, rotating tornadoes and the full-core rotating mills observed in fish collectives. Here we show that locally interacting agents modelled with a minimal cognitive system can produce these collective patterns. We obtained this result by using recent advances in reinforcement learning to systematically solve the inverse modelling problem: given an observed collective behavior, we automatically find a policy generating it. Our agents are modelled as processing the information from neighbour agents to choose actions with a neural network and move in an environment of simulated physics. Even though every agent is equipped with its own neural network, all agents have the same network architecture and parameter values, ensuring in this way that a single policy is responsible for the emergence of a given pattern. We find the final policies by tuning the neural network weights until the produced collective behaviour approaches the desired one. By using modular neural networks with modules using a small number of inputs and outputs, we built an interpretable model of collective motion. This enabled us to analyse the policies obtained. We found a similar general structure for the four different collective patterns, not dissimilar to the one we have previously inferred from experimental zebrafish trajectories; but we also found consistent differences between policies generating the different collective pattern, for example, repulsion in the vertical direction for the more three-dimensional structures of the sphere and tornado. Our results illustrate how new advances in artificial intelligence, and specifically in reinforcement learning, allow new approaches to analysis and modelling of collective behavior.

A novel clinical decision support system for liver fibrosis using evolutionary multi-objective method based numerical association analysis

Chronic liver diseases are among the major health problems in the world. Determining the degree of fibrosis and structural changes and early diagnosis is an important indicator for the course of chronic liver disease, screening of complications and response to treatment. Considering the prevalence of the disease, the use of an invasive biopsy method does not seem practical. At least a preliminary assessment should be able to determine which patients should have a biopsy. In addition, it is not possible to repeat the liver biopsy frequently to follow the course of the patients. Liver biopsy is expensive and it cannot be performed in every hospital. Difficulties in the application for physicians and patients, sampling errors, differences in evaluation, the requirement of a trained physician, difficulties to repeat, and serious complications during the procedure are other disadvantages. The association rule discovery aims to find interesting and valuable associations within the data. Although association analysis is a very useful and popular task in data mining, as far as we know, there is not any study about association analysis of liver fibrosis. We hypothesize at this work that, evolutionary multi-objective methods can be very efficiently modeled and adapted for the automatic miner of comprehensible, accurate, and interesting numerical positive and negative association rules in liver fibrosis clinical decision making. Due to the numerical valued attributes in liver fibrosis data, for the first time, evolutionary intelligent MOPNAR was handled as a rule miner from liver fibrosis without using any discretizing process that requires domain experts. The algorithms modeled for a clinical decision support system in this study modify and adapt themselves for automatic discovery of numerical association rules and do not require modifying or changing the data. Sensitivity analysis of MOPNAR for liver fibrosis was also performed for the first time and a better parameter setting for this task was presented. According to the discovered rules in liver fibrosis data, the MOPNAR outperformed the compared method with respect to average confidence, lift, certainty factor, netconf, yulesQ, number of attributes, and number of covered records.