Mining streaming data is an essential task in many applications such as network intrusion, marketing, manufacturing and others. The main challenge in the streaming data model is its unbounded size. This makes it difficult to run traditional mining techniques on this model. In this paper, we propose a new approach for mining emerging patterns (EPs) in data streams. Our method is based on mining EPs in a selective manner. EPs are those itemsets whose frequencies in one class are significantly higher than their frequencies in the other classes. Our experimental evaluation proves that our approach is capable of gaining important knowledge from data streams.

The naive Bayesian (NB) classifier is one of the simple yet powerful classification methods. One of the important problems in NB (and many other classifiers) is that it is built using crisp classes assigned to the training data. In this paper, we propose an improvement over the NB classifier by employing emerging patterns (EPs) to weight the training instances. That is, we generalize the NB classifier so that it can take into account weighted classes assigned to the training data. EPs are those itemsets whose frequencies in one class are significantly higher than their frequencies in the other classes. Our experiments prove that our proposed method is superior to the original NB classifier.

Decision trees (DTs) represent one of the most important and popular solutions to the problem of classification. They have been shown to have excellent performance in the field of data mining and machine learning. However, the problem of DTs is that they are built using data instances assigned to crisp classes. In this paper, we generalize decision trees so that they can take into account weighted classes assigned to the training data instances. Moreover, we propose a novel method for discovering weights for the training instances. Our method is based on emerging patterns (EPs). EPs are those itemsets whose supports (probabilities) in one class are significantly higher than their supports (probabilities) in the other classes. Our experimental evaluation shows that the new proposed method has good performance and excellent noise tolerance.

Classification is a major problem in machine learning. Many classifiers have been developed recently. However, the performance of these classifiers is proportional to the knowledge obtained from the training data. As a result, traditional classifiers can not perform very well when the training data space is very limited. In this paper, we propose a new approach to expand the training data space (ETDS) using emerging patterns (EPs) [4] and genetic methods (GMs) [7]. EPs are those itemsets whose supports in one class are significantly higher than their supports in the other classes. GMs are evolutionary methods that incorporate computational techniques inspired by biology [8]. We combine the power of EPs and GMs to expand the training data space before applying standard classifiers. The expansion process is performed by generating more training instances using four techniques. An extensive experimental evaluation carried out on a number of datasets shows that our approach has a great impact on the performance of many traditional classifiers.

A data stream model has been proposed recently for those data intensive applications such as financial applications, manufacturing, and others (Babcock et al., 2002). In this model, data arrives in multiple, continuous, rapid, time-varying data streams. These characteristics make it infeasible for traditional classification and mining techniques to deal with data streams. In this paper, we propose a novel method for mining emerging patterns (EPs) in data streams. Moreover, we show how these EPs can be used to classify data streams. EPs (Dong and Li, 1999) are those itemsets whose supports in one class are significantly higher than their supports in the other classes. The experimental evaluation shows that our proposed method can achieve up to 10% increase in accuracy compared to the other methods.

The problem of classifying rarely occurring cases is faced in many real life applications. The scarcity of the rare cases makes it difficult to classify them correctly using traditional classifiers. In this paper, we propose an approach to use emerging patterns (EPs) (G. Dong and J. Li, 1999) and decision trees (DTs) in rare-class classification (EPDT). EPs are those itemsets whose supports in one class are significantly higher than their supports in the other classes. EPDT employs the power of EPs to improve the quality of rare-case classification. To achieve this aim, we first introduce the idea of generating nonexisting rare-class instances, and then we over-sample the most important rare-class instances. Our experiments show that EPDT outperforms many classification methods.