Abstract
The combination of top-K network representation of the data stream with community detection is a novel approach to streaming networks sampling. Keeping an always up-to-date sample of the full network, the advantage of this method, compared to previous, is that it preserves larger communities and original network distribution. Empirically, it will also be shown that these techniques, in conjunction with community detection, provide effective ways to perform sampling and analysis of large scale streaming networks with power law distributions.

Abstract
Knowledge discovery on biomedical data can be based on on-line, data-stream analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in the processes that generate data or in their quality features through time may hinder either the knowledge discovery process or the generalization of past knowledge. These problems can be seen as a lack of data temporal stability. This work establishes the temporal stability as a data quality dimension and proposes new methods for its assessment based on a probabilistic framework. Concretely, methods are proposed for (1) monitoring changes, and (2) characterizing changes, trends and detecting temporal subgroups. First, a probabilistic change detection algorithm is proposed based on the Statistical Process Control of the posterior Beta distribution of the Jensen-Shannon distance, with a memoryless forgetting mechanism. This algorithm (PDF-SPC) classifies the degree of current change in three states: In-Control, Warning, and Out-of-Control. Second, a novel method is proposed to visualize and characterize the temporal changes of data based on the projection of a non-parametric information-geometric statistical manifold of time windows. This projection facilitates the exploration of temporal trends using the proposed IGT-plot and, by means of unsupervised learning methods, discovering conceptually-related temporal subgroups. Methods are evaluated using real and simulated data based on the National Hospital Discharge Survey (NHDS) dataset.

Abstract
The majority of evolving data streams classification algorithms assume that the actual labels of the predicted examples are readily available without any time delay just after a prediction is made. However, given the high label costs, dependence of an expert, limitations in data transmission or even restrictions imposed by the problem's nature, there is a large number of real-world applications in which the availability of actual labels is infinitely delayed (never available), In these cases, it is necessary the use of algorithms that does not follow the traditional process of monitoring the error rate to detect changes in data distribution and uses the most recent labeled data to update the classification model. In this paper, we propose the method Maasstfication to classify evolving data streams with infinitely delayed labels. Our method is inspired on the use of Micro-Cluster representation from online clustering algorithms. Considering the presence of incremental drifts, our approach uses a distance-based strategy to maintain the Micro-Clusters' positions updated. An evaluation in several synthetic and real data shows that Maassification achieves competitive accuracy results to state-of-the-art methods and adequate computational cost. The main advantage of the proposed method is the absence of critical parameters that require user's prior knowledge, as occurs with rival methods.

Abstract
The ever increasing data generation confronts us with the problem of handling online massive amounts of information. One of the biggest challenges is how to extract valuable information from these massive continuous data streams during single scanning. In a data stream context, data arrive continuously at high speed; therefore the algorithms developed to address this context must be efficient regarding memory and time management and capable of detecting changes over time in the underlying distribution that generated the data. This work describes a novel method for the task of pattern classification over a continuous data stream based on an associative model. The proposed method is based on the Gamma classifier, which is inspired by the Alpha-Beta associative memories, which are both supervised pattern recognition models. The proposed method is capable of handling the space and time constrain inherent to data stream scenarios. The Data Streaming Gamma classifier (DS-Gamma classifier) implements a sliding window approach to provide concept drift detection and a forgetting mechanism. In order to test the classifier, several experiments were performed using different data stream scenarios with real and synthetic data streams. The experimental results show that the method exhibits competitive performance when compared to other state-of-the-art algorithms.

Abstract
We propose a concept drift detection method utilizing statistical change detection in which a drift detection method and the Page-Hinkley test are employed. Our method enables users to annotate clustering results without constructing a model of drift detection for every input. In our experiments using synthetic data, we evaluated our proposed method on the basis of detection delay and false detection, also revealed relations between the degree of drift and parameters of the method.

Abstract
Data stream classification algorithms for nonstationary environments frequently assume the availability of class labels, instantly or with some lag after the classification. However, certain applications, mainly those related to sensors and robotics, involve high costs to obtain new labels during the classification phase. Such a scenario in which the actual labels of processed data are never available is called extreme verification latency. Extreme verification latency requires new classification methods capable of adapting to possible changes over time without external supervision. This paper presents a fast, simple, intuitive and accurate algorithm to classify nonstationary data streams in an extreme verification latency scenario, namely Stream Classification Algorithm Guided by Clustering - SCARGC. Our method consists of a clustering followed by a classification step applied repeatedly in a closed loop fashion. We show in several classification tasks evaluated in synthetic and real data that our method is faster and more accurate than the state-of-the-art. Copyright © SIAM.