Abstract

Abstract
Concept drift in data is usually considered only as abrupt or gradual thus referring to the speed of change. Such simple distinguishing by speed is sufficient for most of the problems, but there might be situations for which a finer representation would be of use. This paper studies further the phenomenon of concept drift and introduces a simple measure which is relevant to the speed and amount of change between different concepts.

Abstract
Producing good quality clustering of data streams in real time is a difficult problem, since it is necessary to perform the analysis of data points arriving in a continuous style, with the support of quite limited computational resources. The incremental and evolving nature of the resulting clustering structures must reflect the dynamics of the target data stream. The WiSARD weightless perceptron, and its associated DRASiW extension, are intrinsically capable of, respectively, performing one-shot learning and producing prototypes of the learnt categories. This work introduces a simple generalization of RAM-based neurons in order to explore both weightless neural models in the data stream clustering problem.

Abstract
Histograms are a common technique for density estimation and they have been widely used as a tool in exploratory data analysis. Learning histograms from static and stationary data is a well known topic. Nevertheless, very few works discuss this problem when we have a continuous flow of data generated from dynamic environments. The scope of this paper is to detect changes from high-speed time-changing data streams. To address this problem, we construct histograms able to process examples once at the rate they arrive. The main goal of this work is continuously maintain a histogram consistent with the current status of the nature. We study strategies to detect changes in the distribution generating examples, and adapt the histogram to the most recent data by forgetting outdated data. We use the Partition Incremental Discretization algorithm that was designed to learn histograms from high-speed data streams. We present a method to detect whenever a change in the distribution generating examples occurs. The base idea consists of monitoring distributions from two different time windows: the reference window, reflecting the distribution observed in the past; and the current window which receives the most recent data. The current window is cumulative and can have a fixed or an adaptive step depending on the distance between distributions. We compared both distributions using Kullback-Leibler divergence, defining a threshold for change detection decision based on the asymmetry of this measure. We evaluated our algorithm with controlled artificial data sets and compare the proposed approach with nonparametric tests. We also present results with real word data sets from industrial and medical domains. Those results suggest that an adaptive window's step exhibit high probability in change detection and faster detection rates, with few false positives alarms.

Abstract

Abstract