Elaine Ribeiro de Faria

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Data stream mining is an active research area that has recently emerged to discover knowledge from large amounts of continuously generated data. In this context, several data stream clustering algorithms have been proposed to perform unsupervised learning. Nevertheless, data stream clustering imposes several challenges to be addressed, such as dealing with(More)
Novelty detection has been presented in the literature as one-class problem. In this case, new examples are classified as either belonging to the target class or not. The examples not explained by the model are detected as belonging to a class named novelty. However, novelty detection is much more general, especially in data streams scenarios, where the(More)
Data stream mining is an emergent research area that aims at extracting knowledge from large amounts of continuously generated data. Novelty detection (ND) is a classification task that assesses if one or a set of examples differ significantly from the previously seen examples. This is an important task for data stream, as new concepts may appear, disappear(More)
The objective of this paper is to describe a new approach for locating the centromere of each chromosome displayed in the digitalized photomicrography of fish cells. To detect the centromere position, the authors propose methods for both image segmentation and split touching chromosomes based on the fuzzy sets theory and a method for the rotation of(More)
Data stream mining is an emergent research area that investigates knowledge extraction from large amounts of continuously generated data, produced by non-stationary distribution. Novelty detection, the ability to identify new or previously unknown situations, is a useful ability for learning systems, especially when dealing with data streams, where concepts(More)
Many data stream clustering algorithms operate in two well-defined steps: (i) online statistical data collection stage; and (ii) offline macro-clustering stage. The well-known <i>k</i>-means algorithm is often employed for performing the offline macro-clustering step. The conventional <i>k</i>-means algorithm assumes that the number of clusters (<i>k</i>)(More)
Novelty detection is a useful ability for learning systems, especially in data stream scenarios, where new concepts can appear, known concepts can disappear and concepts can evolve over time. There are several studies in the literature investigating the use of machine learning classification techniques for novelty detection in data streams. However, there(More)
Nowadays, the amount of customers using clothing sites for shopping is greatly increasing, mainly due to the easiness and rapidity of this way of consumption. In this context, Recommender Systems (RS) have become indispensable to help consumers to find products that may possibly pleasant or be useful to them. These systems often use techniques of(More)
In massive data analysis, data usually come in streams. In the last years, several studies have investigated novelty detection in these data streams. Different approaches have been proposed and validated in many application domains. A review of the main aspects of these studies can provide useful information to improve the performance of existing(More)
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