Set Of Class Association Rules Research Articles

C-MWCAR: Classification Based on Multiple Weighted Class Association Rules

Classification is a very important task in data mining and pattern analysis, which have been widely used to solve various real-world problems. To obtain better classification performance, in this paper, we propose a novel classification framework based on multiple weighted class association rules (C-MWCAR), whose key idea is to transform the association among features into a set of class association rules (CARs), then classify unknown instances based on the CARs obtained. Concretely, C-MWCAR consists of a dictionary order-based CAR mining algorithm (DOCMA), a branch-based CAR selection algorithm (BCSA), and a multiple weighted CARs-based classifier (MWCC). Specifically, DOCMA mines the complete set of CARs, from which BCSA further selects a representative and concise set of CARs based on the distribution, coverage, and redundancy of the mined CARs. When classifying an unknown instance, MWCC picks out a set of CARs that are most similar to the given instance and computes the weighted importance of those CARs. Finally, the class label of the given instance will be determined by the similarities between the instance and the CARs and the weighted importance of the CARs. Furthermore, we apply the proposed C-MWCAR to a real-world classification task, i.e., hypertension diagnosis, based on a real dataset of 128 subjects. Experimental results indicate that C-MWCAR outperforms four baseline methods and achieves 93.3%, 93.8%, and 92.7% in terms of accuracy, sensitivity, and specificity, respectively.

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

Classification of Pruning Methodologies for Model Development using Data Mining Techniques

Knowledge discovery process deals with two essential data mining techniques, association and classification. Classification produces a set of large number of associative classification rules for a given observation. Pruning removes unnecessary class association rules without losing classification accuracy. These processes are very significant but at the same time very challenging. The experimental results and limitations of existing class association rules mining techniques have shown that there is a requirement to consider more pruning parameters so that the size of classifier can be further optimized. Here through this paper we are presenting a survey various strategies for class association rule pruning and study their effects that enables us to extract efficient compact and high confidence class association rule set and we have also proposed a pruning methodology

Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining.

Hypertension is one of the most common cardiovascular diseases, which will cause severe complications if not treated in a timely way. Early and accurate identification of hypertension is essential to prevent the condition from deteriorating further. As a kind of complex physiological state, hypertension is hard to characterize accurately. However, most existing hypertension identification methods usually extract features only from limited aspects such as the time-frequency domain or non-linear domain. It is difficult for them to characterize hypertension patterns comprehensively, which results in limited identification performance. Furthermore, existing methods can only determine whether the subjects suffer from hypertension, but they cannot give additional useful information about the patients’ condition. For example, their classification results cannot explain why the subjects are hypertensive, which is not conducive to further analyzing the patient’s condition. To this end, this paper proposes a novel hypertension identification method by integrating classification and association rule mining. Its core idea is to exploit the association relationship among multi-dimension features to distinguish hypertensive patients from normotensive subjects. In particular, the proposed method can not only identify hypertension accurately, but also generate a set of class association rules (CARs). The CARs are proved to be able to reflect the subject’s physiological status. Experimental results based on a real dataset indicate that the proposed method outperforms two state-of-the-art methods and three common classifiers, and achieves 84.4%, 82.5% and 85.3% in terms of accuracy, precision and recall, respectively.

Interpretable regularized class association rules algorithm for classification in a categorical data space

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.

Prediction of Press-Fit Quality via Data Mining Techniques and Artificial Intelligence

Determining the press-fit quality of pieces in advance is of the utmost importance because it enables the reduction of the time that is invested in the process and the prevention of material losses. High predictive accuracy is essential in a classification model; however, several studies have shown that the class category of a new instance may be insufficient information for decision making. To provide additional information to the user, this study presents a novel system that is based on a hybrid model, which, in addition, to using a classifier, extracts a set of class association rules that enable the determination of which patterns influence the new instance to belong to a class category. To select the classifier, the accuracy, recall and F-measure metrics were utilized. The rules were obtained with the Apriori algorithm to show that this knowledge is automatically represented in an ontological scheme with the objective of applying the Pellet reasoner.

A novel method for constrained class association rule mining

To create a classifier using an associative classification algorithm, a complete set of class association rules (CARs) is obtained from the training dataset. Most generated rules, however, are either redundant or insignificant. They not only confuse end users during decision-making but also decrease the performance of the classification process. Thus, it is necessary to eliminate redundant or unimportant rules as much as possible before they are used. A related problem is the discovery of interesting or useful rules. In existing classification systems, the set of such rules may not be discovered easily. However, in real world applications, end users often consider the rules with consequences that contain one of particular classes. For example, in cancer screening applications, researchers are very interested in rules that classify genes into the “cancer” class. This paper proposes a novel approach for mining relevant CARs that considers constraints on the rule consequent. A tree structure for storing frequent itemsets from the dataset is designed. Then, some theorems for pruning tree nodes that cannot generate rules satisfying the class constraints are provided and proved. Finally, an efficient algorithm for mining constrained CARs is presented. Experiments show that the proposed method is faster than existing methods.

RST Approach for Efficient CARs Mining

In data mining, an association rule is a pattern that states the occurrence of two items (premises and consequences) together with certain probability. A class association rule set (CARs) is a subset of association rules with classes specified as their consequences. This paper focuses on class association rules mining based on the approach of Rough Set Theory (RST). In addition, this paper presents an algorithm for finest class rule set mining inspired from Apriori algorithm, where the support and confidence are computed based on the elementary set of lower approximation inspired from RST. The proposed approach has been shown very effective, where the rough set approach for class association discovery is much simpler than the classic association method.

Tendency Mining in Dynamic Association Rules Based on SVM Classifier

A method of tendency mining in dynamic association rule based on compatibility feature vector SVM classifier is proposed. Firstly, the class association rule set named CARs is mined by using the method of tendency mining in dynamic association rules. Secondly, the algorithm of SVM is used to construct the classifier based on compatibility feature vector to classify the obtained CARs taking advantage when dealing with high complex data. It uses a method based on judging rules’ weight to construct the model. At last, the method is compared with the traditional methods with respect to the mining accuracy. The method can solve the problem of high time complexity and have a higher accuracy than the traditional methods which is helpful to make mining dynamic association rules more accurate and effective. By analyzing the final results, it is proved that the method has lower complexity and higher classification accuracy.

Analysis of Associative Classification for Prediction of HCV Response to Treatment

objective of this research is the analysis of predicting the response for treatment in patient with hepatitis C virus. The Interferon Alfa (IFN) in combination with ribavirin (RBV) is used as a standard therapy for chronic hepatitis C (CHC), it is very expensive and accompanied with great side effects, with that it fails in more than half cases. For the prediction of treatment response, a knowledge discovery framework including two main phases: pre- processing and data mining was developed. In pre- processing phase, the cleaning and selection of suitable features from patients' data were done. In data mining phase the selected patients' features were mined using Associative Classification (AC) technique to generate a set of Class Association Rules (CARs). The most suitable rules from the generated CARs were selected to build a classifier, which predicts patients' response for treatment. Using our classification model, 220 patients treated with IFN plus RBV were analyzed, 92 patients resulted responders and 128 non- responders at the end of treatment and during the follow up. 170 cases had been used to train our intelligent models and 50 patients had been used to test the models. The experiment results showed that the proposed technique is an effective classification technique with high prediction accuracy reach up to 90%.

Class Association Rule Pada Metode Associative Classification

Frequent patterns (itemsets) discovery is an important problem in associative classification rule mining. Differents approaches have been proposed such as the Apriori-like, Frequent Pattern (FP)-growth, and Transaction Data Location (Tid)-list Intersection algorithm. This paper focuses on surveying and comparing the state of the art associative classification techniques with regards to the rule generation phase of associative classification algorithms. This phase includes frequent itemsets discovery and rules mining/extracting methods to generate the set of class association rules (CARs). There are some techniques proposed to improve the rule generation method. A technique by utilizing the concepts of discriminative power of itemsets can reduce the size of frequent itemset. It can prune the useless frequent itemsets. The closed frequent itemset concept can be utilized to compress the rules to be compact rules. This technique may reduce the size of generated rules. Other technique is in determining the support threshold value of the itemset. Specifying not single but multiple support threshold values with regard to the class label frequencies can give more appropriate support threshold value. This technique may generate more accurate rules. Alternative technique to generate rule is utilizing the vertical layout to represent dataset. This method is very effective because it only needs one scan over dataset, compare with other techniques that need multiple scan over dataset. However, one problem with these approaches is that the initial set of tid-lists may be too large to fit into main memory. It requires more sophisticated techniques to compress the tid-lists.

Mining the optimal class association rule set

We define an optimal class association rule set to be the minimum rule set with the same predictive power of the complete class association rule set. Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We present an efficient algorithm for mining the optimal class association rule set using an upward closure property of pruning weak rules before they are actually generated. We have implemented the algorithm and our experimental results show that our algorithm generates the optimal class association rule set, whose size is smaller than 1/17 of the complete class association rule set on average, in significantly less rime than generating the complete class association rule set. Our proposed criterion has been shown very effective for pruning weak rules in dense databases.