Abstract
Nowadays, there are applications in which the data are modeled best not as persistent tables, but rather as transient data streams. In this article, we discuss the limitations of current machine learning and data mining algorithms. We discuss the fundamental issues in learning in dynamic environments like continuously maintain learning models that evolve over time, learning and forgetting, concept drift and change detection. Data streams produce a huge amount of data that introduce new constraints in the design of learning algorithms: limited computational resources in terms of memory, cpu power, and communication bandwidth. We present some illustrative algorithms, designed to taking these constrains into account, for decision-tree learning, hierarchical clustering and frequent pattern mining. We identify the main issues and current challenges that emerge in learning from data streams that open research lines for further developments. © 2011 Springer-Verlag.

Abstract

Abstract
It is well known that the definition of bus schedules is critical for the service reliability of public transports. Several proposals have been suggested, using data from Automatic Vehicle Location (AVL) systems, in order to enhance the reliability of public transports. In this paper we study the optimum number of schedules and the days covered by each one of them, in order to increase reliability. We use the Dynamic Time Warping distance in order to calculate the similarities between two different dimensioned irregularly spaced data sequences before the use of data clustering techniques. The application of this methodology with the K-Means for a specific bus route demonstrated that a new schedule for the weekends in non-scholar periods could be considered due to its distinct profile from the remaining days. For future work, we propose to apply this methodology to larger data sets in time and in number, corresponding to different bus routes, in order to find a consensual cluster between all the routes. ©2010 IEEE.

Abstract
As sensors become ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including disaster preparedness and management, adaptability to climate change, national or homeland security, and the management of critical infrastructures. This book presents innovative solutions in offline data mining and real-time analysis of sensor or geographically distributed data. It discusses the challenges and requirements for sensor data based knowledge discovery solutions in high-priority application illustrated with case studies. It explores the fusion between heterogeneous data streams from multiple sensor types and applications in science, engineering, and security. © 2009 by Taylor & Francis Group, LLC.

Abstract

Abstract
A machine learning approach that is capable of treating data streams presents new challenges and enables the analysis of a variety of real problems in which concepts change over time. In this scenario, the ability to identify novel concepts as well as to deal with concept drift axe two important attributes. This paper presents a technique based on the k-means clustering algorithm aimed at considering those two situations in a single learning strategy. Experimental results performed with data from various domains provide insight into how clustering algorithms can be used for the discovery of new concepts in streams of data.