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Based on association rules, Associative classification (AC) has shown great promise over many other classification techniques on static dataset. However, the increasing prominence of data streams arising in a wide range of advanced application has posed a new challenge for it. This paper describes and evaluates AC-DS, a new associative classification algorithm for data streams which is based on the...
The application of associative rule mining in classification (associative classification) has demonstrated its power in recent years. The current associative classifier building often adopts three phases: Rule Generation, Building Classifier and Classification. Unfortunately, in rule generation phase, a large number of rules are usually produced, which could not only slow down the mining process but...
Hierarchical Classification is a very important classification task for arranging data in a hierarchical structure. Hierarchical arrangement of data is one of the best methods to achieve better understanding of complex data. In this paper, we propose the HMAC method to perform Hierarchical Multi-label Associative Classification. This method uses multiple and negative rules to predict class-set and...
Associative classification presents various methods whose common characteristic is the class prediction from the class association rules (rules whose consequent one is one of the class modalities). According to and, this new approach offers better results than the traditional approaches based on rules such as the decision trees. It also offers a great flexibility with the unstructured data. However,...
The application of association rule mining to classification has led to a new family of classifiers which are often referred to as Associative Classifiers (ACs). An advantage of ACs is that they are rule-based and thus lend themselves to an easier interpretation. However, it is common knowledge that association rule mining typically yields a sheer number of rules defeating the purpose of a human readable...
Classification is an important subject in data mining and machine learning, which has been studied extensively and has a wide range of applications. Classification based on association rules is one of the most effective classification method, whose accuracy is higher and discovered rules are easier to understand comparing with classical classification methods. However, current algorithms for classification...
Classification aims to define an abstract model of a set of classes, called classifier, which is built from a set of labeled data, the training set. However, in large or correlated data sets, association rule mining may yield huge rule sets. Hence several pruning techniques have been proposed to select a small subset of high-quality rules. Since the availability of a ldquorichrdquo rule set may improve...
Recent studies in data mining have proposed a new classification approach, called associative classification, which, according to several reports, such as , achieves higher classification accuracy than traditional classification approaches such as C4.5. However, the approach also suffers from one major deficiency: a training data set often generates a huge set of rules. It is challenging to store,...
Numerous attacks made by the malware have presented serious threats to the security of computer users. Unfortunately, along with the development of the malware writing techniques, the number of file samples that need to be analyzed is constantly increasing on a daily basis. An automatic and robust tool to analyze and classify the file samples is the need of the hour. In this paper, resting on the...
Classification using association rules has added a new dimension to the ongoing research for accurate classifiers. Experiments have shown that these classifiers are significantly more accurate than decision tree classifiers. The idea behind most of the existing approaches has been the mining of positive class association rules from the training set and then selecting a subset of the mined rules for...
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