Class Association Rules Research Articles

Decision Model for Unplanned ICU Transfer in a Hospital with Association Rule Learning

The initial decision after treatment in the hospital emergency room is very important because apart from being an indicator of the quality of care for emergency room practitioners, it is also needed to achieve health goals, namely improving the quality of critical care and preventing death. The initial decision was also for an unplanned ICU transfer. Unplanned ICU transfer is the transfer of patients who originally came from the ER (Emergency Room), then to the Inpatient Room (having been treated for 24-48 hours), then to the ICU. Many studies have been carried out to predict the initial decision of unplanned ICU transfer using univariate analysis, logistic regression analysis, and association rules. The association rule algorithm generates rules between patient diagnosis features that form a decision model for unplanned ICU transfers, so it is essential to get an association rule algorithm that is more efficient in generating rules. In this study, we compare two association rule algorithms to get a more efficient algorithm; then, the rules are used to form a decision model for unplanned ICU transfers. The study results obtained that the Apriori algorithm requires a completion time of 3 ms and the FP-Growth algorithm requires a completion time of 31 ms. Hence, the FP-Growth algorithm is 28 ms more efficient than the Apriori algorithm, while the resulting rule generation is the same number of 67 rules. Only 11 rules meet the minsupp and minconf threshold and include the set of Class Association Rules (CAR), which are used to form a decision model for unplanned ICU transfers with binary integer programming

Intelligent Campus Resource Sharing System Based on Data Fusion Sensor

In order to make full use of the large amount of teaching data existing in the data center, the author proposes a mining algorithm using classification technology and association rules in data mining technology; the teaching quality evaluation data and student achievement data were mined. By constructing the C4.5 decision tree algorithm, this method taps the potential links between teachers’ professional titles, degrees, ages, and teaching quality evaluation results, through correlation analysis theory; the correlation between professional courses was mined from the student’s course achievement database, and some reasonable and reliable course association rules were obtained. Experimental results show that the correct rate of C4.5 decision tree algorithm under different sampling times is above 80%. The conclusions drawn have certain guiding significance for college teaching and personnel training.

ItemSB: Itemsets with Statistically Distinctive Backgrounds Discovered by Evolutionary Method

In this paper, we propose a method for discovering combinations of attributes (i.e. itemsets) against a background of statistical characteristics without obtaining frequent itemsets. The method considers a database with numerous attributes and can directly find a combination of highly correlated attributes from small populations in two consecutive variables of interest even from an incomplete database. As the proposed method determines local patterns in large-scale data, it may be used as a basis for large-scale data analysis. Evolutionary computations characterized by a network structure and a strategy to pool solutions are used throughout generations. Moreover, association rules are used to generalize the analysis method as itemsets with statistically distinctive backgrounds (ItemSBs). The class-association rules used for classification constitute a discovery method of attribute combinations, which are characteristic when the ratio of class attributes is obtained. The proposed method is an extension to statistical bivariate analysis. In addition, we determine contrast ItemSBs that are statistically different between two subgroups of data while satisfying the same conditions. Experimental results show the characteristics and effectiveness of the proposed method.

Nursing Diagnosis of Urology Operating Room Based on New Association Classification Algorithm.

Due to the rapid development of medical engineering, massive amounts of data are recorded and preserved by various medical instruments. Therefore, finding relationships among data and summarizing clinical manifestations are of great significance to the diagnosis, treatment, and medical research of various diseases. The key to studying the nursing diagnosis support system, particularly in the urological operating room, is to select an effective classification algorithm, which is suitable for the characteristics of urological diseases. Initially, we have analyzed characteristics of urological diseases through medical data mining. Secondly, based on the traditional data mining classification method and urological disease diagnosis research, we have introduced the urological disease experimental source dataset and analyzed characteristics of the disease. Furthermore, classification algorithm and steps were introduced such as decision tree (including ID3, C4.5), Bayesian classification, BP neural network, and association rule classification algorithms. These algorithms are used to make relevant comparative experiments on the urological disease dataset. Finally, based on the diagnosis of urological diseases, a new association classification algorithm (ACCF), which is based on frequent closed item sets, is proposed along with suitable explanation. In order to verify the operational capabilities, the proposed algorithms are implemented in C++ and compared with the classification effect of traditional association classification algorithms and data mining methods. Both theoretical analysis and experiment results show that the proposed algorithm has resolved various deficiencies of the existing data mining algorithms and equally improved the accuracy of urological disease classification and prediction.

Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm

Evolutionary computation and data mining are two fascinating fields that have attracted many researchers. This paper proposes a new rule mining method, named genetic network programming (GNP), to solve the prediction problem using the evolutionary algorithm. Compared with the conventional association rule methods that do not consider the weight factor, the proposed algorithm provides many advantages in financial prediction, since it can discover relationships among the attributes of different transactions. Experimental results on data from the New York Exchange Market show that the new method outperforms other conventional models in terms of both accuracy and profitability, and the proposed method can establish more important and accurate rules than the conventional methods. The results confirmed the effectiveness of the proposed data mining method in financial prediction.

NEW DISCRETE CROW SEARCH ALGORITHM FOR CLASS ASSOCIATION RULE MINING

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

New Discrete Crow Search Algorithm for Class Association Rule Mining

Associative Classification (AC) or Class Association Rule (CAR) mining is a very efficient method for the classification problem. It can build comprehensible classification models in the form of a list of simple IF-THEN classification rules from the available data. In this paper, we present a new, and improved discrete version of the Crow Search Algorithm (CSA) called NDCSA-CAR to mine the Class Association Rules. The goal of this article is to improve the data classification accuracy and the simplicity of classifiers. The authors applied the proposed NDCSA-CAR algorithm on eleven benchmark dataset and compared its result with traditional algorithms and recent well known rule-based classification algorithms. The experimental results show that the proposed algorithm outperformed other rule-based approaches in all evaluated criteria.

Improved imputation of rule sets in class association rule modeling: application to transportation mode choice

Improved imputation of rule sets in class association rule modeling: application to transportation mode choice

Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care.

We propose a novel method that uses associative classification and odds ratios to predict in-hospital mortality in emergency and critical care. Manual mortality risk scores have previously been used to assess the care needed for each patient and their need for palliative measures. Automated approaches allow providers to get a quick and objective estimation based on electronic health records. We use association rule mining to find relevant patterns in the dataset. The odds ratio is used instead of classical association rule mining metrics as a quality measure to analyze association instead of frequency. The resulting measures are used to estimate the in-hospital mortality risk. We compare two prediction models: one minimal model with socio-demographic factors that are available at the time of admission and can be provided by the patients themselves, namely gender, ethnicity, type of insurance, language, and marital status, and a full model that additionally includes clinical information like diagnoses, medication, and procedures. The method was tested and validated on MIMIC-IV, a publicly available clinical dataset. The minimal prediction model achieved an area under the receiver operating characteristic curve value of 0.69, while the full prediction model achieved a value of 0.98. The models serve different purposes. The minimal model can be used as a first risk assessment based on patient-reported information. The full model expands on this and provides an updated risk assessment each time a new variable occurs in the clinical case. In addition, the rules in the models allow us to analyze the dataset based on data-backed rules. We provide several examples of interesting rules, including rules that hint at errors in the underlying data, rules that correspond to existing epidemiological research, and rules that were previously unknown and can serve as starting points for future studies.

Implementation of Lung Cancer Diagnosis based on DNN in Healthcare System

Cancer represents a kind of disease that is widespread throughout the world. Actually, there are several kinds of cancer. However, lung cancer represents the most prevalent cancer form and can lead to death with late healthcare. Therefore, it is essential to initialize therapy via diagnosing lung cancer for decreasing the death chance. Classification is one of the fundamental issues in the knowledge discovery fields and scientific decisions. There are many types of techniques used for constructing classifiers and cancer diagnosis. Recently, deep learning becomes a powerful and popular classification technique for many areas of medical data diagnosis in the healthcare systems. In this paper, an effective and accurate deep neural network (DNN) based lung cancer diagnosis implemented in the healthcare system has been proposed which includes three main phases; pre-processing, generating strong rules, and classification. The input data are pre-processed in the first phase. Because these data are entered from databases, so there are missing data that should be replaced with zero values. Then, data are normalized for speeding up the learning phase. After that, the class association rule is used to enhance the classification performance by generating frequent patterns inducible from the dataset which include features that are significant to the class attribute. Finally, DNN is utilized in the process of classification for obtaining a sample diagnosis estimate. DNN based diagnosis system was tested and evaluated on the lung cancer dataset which has 25 attributes and 1000 instances. The obtained results demonstrated that the proposed system achieved a high performance compared to other existing lung cancer diagnosis systems with 95% accuracy, 97% specificity, and 95% sensitivity.

Semi-supervised associative classification using ant colony optimization algorithm.

Labeled data is the main ingredient for classification tasks. Labeled data is not always available and free. Semi-supervised learning solves the problem of labeling the unlabeled instances through heuristics. Self-training is one of the most widely-used comprehensible approaches for labeling data. Traditional self-training approaches tend to show low classification accuracy when the majority of the data is unlabeled. A novel approach named Self-Training using Associative Classification using Ant Colony Optimization (ST-AC-ACO) has been proposed in this article to label and classify the unlabeled data instances to improve self-training classification accuracy by exploiting the association among attribute values (terms) and between a set of terms and class labels of the labeled instances. Ant Colony Optimization (ACO) has been employed to construct associative classification rules based on labeled and pseudo-labeled instances. Experiments demonstrate the superiority of the proposed associative self-training approach to its competing traditional self-training approaches.

Technique for Optimization of Association Rule Mining by Utilizing Genetic Algorithm

Background: Due to advancement in usage of Internet and pattern discovery from huge amount of data flowing through internet, personal information of an individual or organization can be traced. Hence, to protect the private information is becoming extremely crucial, which can be achieved through privacy preserving data mining. Objective: The main objective to preserve the privacy of data and to maintain the balance between privacy and accuracy by applying privacy preserving technique and optimization respectively. Methodology: The generation of class association rule is done by utilizing associative classification technique namely class based association due to its simplicity which serves the purpose of classifying the data. Furthermore, privacy of the data should be maintained and hence privacy preserved class association rules are produced by applying privacy preserved technique namely anonymization. Hence, optimization technique specifically genetic algorithm as well as neural network has been applied to maximize the accuracy. Results: (Four various real datasets has been utilized for different experimentation). : Implemented Classification Based on Association (CBA) algorithm of Associative Classification technique and it provides virtuous accuracy as compared to other techniques by setting the support as 0.2 and confidence at 0.6. : Privacy preserving techniques namely k-anonymization was implemented to preserve the privacy but it has been observed that as privacy (k-level) increases, accuracy (percentage) decreases due to data transformation. Conclusion: (Hence, optimization technique namely Genetic Algorithm (GA) and Neural Network (NN) has been implemented to increase the accuracy (probably 7-8%). Furthermore, on comparison of GA and NN considering the time parameter, GA outperforms well.

ARC-SL: Association rule-based classification with soft labels

As one of the most promising classification schemes, association classification (AC) effectively integrates classification with association rule discovery, but it requires precisely labelled data which is usually expensive or hard to obtain in practice. This paper extends the AC to solve the classification of data with soft labels represented as belief functions defined over the set of classes. To characterize the imprecision of labels, a more general rule, called imprecise classification association rule (ICAR), is first introduced so that the consequent is more flexible, being any subset of the class set. Meanwhile, measures of support and confidence are proposed for ICAR by taking into account the belief functions encoded in soft labels. With the new rule structure, an association rule-based soft labelled classification method, called ARC-SL, is then developed to build a more accurate classification model containing three phases: entropy-based adaptive partition for deriving fuzzy regions of continuous attributes, Apriori-based rule mining for generating a set of ICARs with variable support and confidence thresholds, and rule pruning for discarding redundant or poor rules. Finally, a belief reasoning procedure is presented to classify each input instance through combining the activated rules in the framework of belief functions. Experiments on benchmark datasets and an application to facial expression recognition demonstrate the effectiveness of the proposed method.

Pushing towards ehealth for iraqi hypertensives: an integrated class association rules into SECI model

<span lang="EN-GB">This paper highlights the barriers that have led to a delay in the implementation of E-Health services in Iraq. A new framework is proposed to improve the E-Health sector using a SECI model which describes how explicit and tacit knowledge is generated, transferred, and recreated in organizations through main stages (socialization, externalization, combination and internalization). Class association rules (CARs) is integrated to mine the SECI model by extracting related rules which correspond to the medical advice. The proposed framework (SECICAR) can be done through a web portal to assemble healthcare professionals, patients in one environment. SECICAR will be applied to the hypertension community to show that disease if left untreated, frequently leads to serious illnesses such as heart disease. The SECICAR aims to facilitate the dissemination of tacit knowledge, which is explicit to hypertensives, in the form of strategies, guidelines and best practices. The validation of the SECICAR results displays satisfactory accuracy and reliability. Heuristic evaluation was used to test the web portal, the participants stating that there were no major issues regarding its usability.</span>

IHAC: Incorporating Heuristics for Efficient Rule Generation & Rule Selection in Associative Classification

Associations rule mining along with classification rule mining are both significant techniques of mining of knowledge in the area of knowledge discovery in massive databases stored in different geographic locations of the world. Based on such combination of these two, class association rules for mining or associative classification methods have been generated, which, in far too many cases, showed higher prediction accuracy than platitudinous conventional classifiers. Motivated by the study, in this paper, we proposed a new approach, namely IHAC (Incorporating Heuristics for efficient rule generation & rule selection in Associative Classification). First, it utilises the database to decrease the search space and then explicitly explores the potent class association rules from the optimised database. This also blends rule generation and classifier building to speed up the overall classifier construction cycle. Experimental findings showed that IHAC performs better than any further associative classification methods.

STAR

Timed association rules (TARs) generalize classical association rules (ARs) so that we can express temporal dependencies of the form “If X is true at time t , then Y will likely be true at time ( t +τ).” As with ARs, solving the TAR mining problem can generate huge numbers of rules. We show that methods to summarize ARs cannot work directly with TARs, and we develop two notions— strong and weak summaries—to summarize a set of TARs. We show that the problems of finding strong/weak summaries are NP-hard, and we provide polynomial-time approximation algorithms. We show experimentally that the coverage provided by our summarization methods is very high. Both technical measures based on coverage and human experiments on six World Bank datasets using 100 subjects from Mechanical Turk and a separate experiment with terrorism experts on a terrorism dataset show that while both summarization methods perform well, weak summaries are preferred, despite their taking more time to compute than strong summaries.

Algorithm Study of New Association Rules and Classification Rules in Data Mining

In order to further improve the efficiency of data mining, it proposes a kind of data mining algorithm based on new association rules and classification rules. Specific research process is as follows. Firstly, MDML-PP algorithm is analyzed and applied to the multi-dimensional multi-level association rules in data mining, and then selects the test data sets for performance evaluation, meanwhile, it also studies the multi-support rate cut and classification rule mining algorithm, which is applied to multi-support rate classification rules in data mining, finally, using the compare experiment to verify the effectiveness of multi-support rate classification rules. The results show that the proposed algorithm in this paper can be better used in data mining with multi-dimensional and multi-layer association rules, which can improve the efficiency and quality of data mining.

CARs-RP: Lasso-based class association rules pruning

CARs-RP: Lasso-based class association rules pruning

Quantitative class association rule-based approach to lecturer career promotion recommendation

This paper introduces an effective method for recommendation of career promotion for educators in universities. Given an educator with an activity profile currently in a certain career state, the system is able to recommend the next career state that is most suitable for the educator by learning the pattern of historical activities of other educators. The system also recommends activities and their volume that the educators should take in order to achieve the career state. Patterns of activities and their volume were obtained using the quantitative class-association rule mining method that classifies quantified activities to a next-state class. The experiment using educator career data taken from Indonesian universities produced several recommendations that were somewhat contrary to the opinions of experts about educators who can make a career leap, an indication that expert subjectivity was more dominant than a statistically more reliable recommendation system in making decisions.

Distance based clustering of class association rules to build a compact, accurate and descriptive classifier

Huge amounts of data are being collected and analyzed nowadays. By using the popular rule-learning algorithms, the number of rule discovered on those ?big? datasets can easily exceed thousands. To produce compact, understandable and accurate classifiers, such rules have to be grouped and pruned, so that only a reasonable number of them are presented to the end user for inspection and further analysis. In this paper, we propose new methods that are able to reduce the number of class association rules produced by ?classical? class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose new associative classifiers, called DC, DDC and CDC, that use distance-based agglomerative hierarchical clustering as a post-processing step to reduce the number of its rules, and in the rule-selection step, we use different strategies (based on database coverage and cluster center) for each algorithm. Experimental results performed on selected datasets from the UCI ML repository show that our classifiers are able to learn classifiers containing significantly fewer rules than state-of-the-art rule learning algorithms on datasets with a larger number of examples. On the other hand, the classification accuracy of the proposed classifiers is not significantly different from state-of-the-art rule-learners on most of the datasets.