Learning from Data Streams

Learning from Data Streams

João Gama (University of Porto, Portugal) and Pedro Pereira Rodrigues (University of Porto, Portugal)
Copyright: © 2009 |Pages: 5
DOI: 10.4018/978-1-60566-010-3.ch176
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

In the last two decades, machine learning research and practice has focused on batch learning usually with small datasets. In batch learning, the whole training data is available to the algorithm that outputs a decision model after processing the data eventually (or most of the times) multiple times. The rationale behind this practice is that examples are generated at random accordingly to some stationary probability distribution. Also, most learners use a greedy, hill-climbing search in the space of models. What distinguishes current data sets from earlier ones are the continuous flow of data and the automatic data feeds. We do not just have people who are entering information into a computer. Instead, we have computers entering data into each other. Nowadays there are applications in which the data is modelled best not as persistent tables but rather as transient data streams. In some applications it is not feasible to load the arriving data into a traditional DataBase Management Systems (DBMS), and traditional DBMS are not designed to directly support the continuous queries required in these application (Babcock et al., 2002). These sources of data are called Data Streams. There is a fundamental difference between learning from small datasets and large datasets. As pointed-out by some researchers (Brain & Webb, 2002), current learning algorithms emphasize variance reduction. However, learning from large datasets may be more effective when using algorithms that place greater emphasis on bias management. Algorithms that process data streams deliver approximate solutions, providing a fast answer using few memory resources. They relax the requirement of an exact answer to an approximate answer within a small error range with high probability. In general, as the range of the error decreases the space of computational resources goes up. In some applications, mostly database oriented, an approximate answer should be within an admissible error margin. Some results on tail inequalities provided by statistics are useful to accomplish this goal.
Chapter Preview
Top

Learning Issues: Online, Anytime And Real-Time Learning

The challenge problem for data mining is the ability to permanently maintain an accurate decision model. This issue requires learning algorithms that can modify the current model whenever new data is available at the rate of data arrival. Moreover, they should forget older information when data is out-dated. In this context, the assumption that examples are generated at random according to a stationary probability distribution does not hold, at least in complex systems and for large time periods. In the presence of a non-stationary distribution, the learning system must incorporate some form of forgetting past and outdated information. Learning from data streams require incremental learning algorithms that take into account concept drift. Solutions to these problems require new sampling and randomization techniques, and new approximate, incremental and decremental algorithms. In (Hulten & Domingos, 2001), the authors identify desirable properties of learning systems that are able to mine continuous, high-volume, open-ended data streams as they arrive: i) incrementality, ii) online learning, iii) constant time to process each example using fixed memory, iv) single scan over the training data, and v) tacking drift into account.

Examples of learning algorithms designed to process open-ended streams include predictive learning: Decision Trees (Domingos & Hulten, 2000; Hulten et al., 2001; Gama et al., 2005, 2006), Decision Rules (Ferrer et al., 2005); descriptive learning: variants of k-Means Clustering (Zhang et al., 1996; Sheikholeslami et al., 1998), Clustering (Guha et al., 1998; Aggarwal et al., 2003), Hierarchical Time-Series Clustering (Rodrigues et al., 2006); Association Learning: Frequent Itemsets Mining (Jiang & Gruemwald, 2006), Frequent Pattern Mining (Jin & Agrawal 2007); Novelty Detection (Markou & Singh, 2003; Spinosa et al. 2007); Feature Selection (Sousa et al., 2006), etc.

Complete Chapter List

Search this Book:
Reset