\setHeader{Monday Morning Tutorials}{Monday}{0}
%\pageTitle{Monday Morning Tutorials}

\tutorialHeader{Advanced Topics in Data Stream Mining} 
{Albert Bifet, Joao Gama, Richard Gavalda, Georg Krempl, Mykola
  Pechenizkiy, \\Bernhard Pfahringer, Myra Spiliopoulou and Indr\.{e}
  \v{Z}liobait\.{e}}
{09:00--12:00}{Room: Wills, 3.31}

\tutorialIntro{Nowadays, the quantity of data that is created every two days is estimated to be 5 exabytes. This amount of data is similar to the  amount of data created from the dawn of time up until 2003. Moreover, it was estimated that 2007 was the first year in which it was not possible to store all the data that we are producing. This massive amount of real time streaming data opens new challenging discovery tasks. Some of them are already addressed with mature algorithms, while new challenges emerge, including learning on not one but multiple streams. This tutorial has two parts. The first part gives an introduction to recent advances in algorithmic techniques and tools to cope with challenges on stream mining. The second part discusses state of the art research on mining multiple streams -- distributed streams and interdependent relational streams.


Concept drift plays a central role in this tutorial. In the first part, we address it in the context of conventional one-stream mining to set the scene. In the second part, we recapitulate on it after introducing multiple-stream mining, and we also consider machine learning methods that are appropriate for incremental data and slow streams.

The first part, `Mining One Stream', will be presented
by Albert Bifet, Ricardo Gavalda, Mykola Pechenizkiy, Bernhard
Pfahringer, and Indr\.{e} \v{Z}liobait\.{e}.

The second part, `Mining Multiple Streams' will be
presented by Joao Gama, Myra Spiliopoulou, and Georg Krempl.}


\tutorialHeader{Advanced Topics in Ensemble Learning}
{Ioannis Partalas, Alberto Suarez, Daniel Hernandez
  Lobato,\break  Grigorios Tsoumakas and Gonzalo Martinez}
{09:00--12:00}{Room: Wills, G25}

\tutorialIntro{Ensemble methods are widely used by the machine learning community because they lead to improvements in accuracy and robustness in many prediction problems of practical interest. Furthermore, they offer appealing solutions to several interesting learning problems, such as dealing with small or large datasets, performing  data fusion and modeling concept drift. Notwithstanding, using ensemble methods also poses some challenges. Specifically, in many problems very large ensembles need to be generated to achieve good generalization performance. Similarly, the ensemble prediction is typically generated by querying all the classifiers contained in the ensemble, a process that can be computationally expensive. In this tutorial we give a brief introduction to ensemble methods for machine learning and describe ensemble pruning techniques to deal with the shortcomings described. We also introduce advanced research topics of current interest, such as the problem of determining the optimal ensemble size or techniques to make ensembles scale to large learning problems.}



\newpage
\setHeader{Monday Afternoon Tutorials}{Monday}{0}
%\pageTitle{Monday Afternoon Tutorials}

\tutorialHeader{Decomposing Binary Matrices: Where Linear
  Algebra Meets Combinatorial Data Mining} 
{Pauli Miettinen}
{13:30--16:00}{Room: Wills, 3.31}

\vspace{-2mm}
\tutorialIntro{The tutorial studies the connection between matrix factorization methods and data mining on binary data (e.g. pattern set mining). On one hand, it shows how many data mining methods can be modeled as discrete matrix factorizations. This change of conceptual framework provides new tools and concepts that help the data miner to generalize the algorithms, develop new algorithms, and analyse the algorithms in new ways. On the other hand, it shows how discretization of matrix factorizations provides new tools and methods for traditional matrix factorization applications.

This tutorial considers the problems of representing a binary matrix as a product of two binary matrices using three different algebras: the normal algebra (where 1+1=2), the modulo-2 algebra (where 1+1=0), and the Boolean algebra (where 1+1=1). In all of these cases, the requirement of binary factor matrices renders most of our normal matrix factorization tools less useful, calling for more combinatorial approaches. The combinatorial nature of the problems further increase when one moves from normal algebra to modulo-2 algebra to Boolean algebra. Nevertheless, all these problems show traces of both linear algebra and combinatorics and solving them requires techniques from both fields. A core idea of this tutorial is to show how ideas and concepts from linear algebra can be translated to combinatorial settings (and vice versa) and how that can be used to solve the factorization problems (and therefore other problems modeled as factorization problems).

The tutorial covers both theory and algorithms for these factorizations. The theory-part concentrates on building the connections (and pointing non-obvious differences) between the discrete and continuous factorizations. Central to this discussion is the concept of matrix rank, and how ranks under different algebras relate to each other. This discussion is also indented to give more intuition on how the different binary factorizations behave. Also the computational complexity of the factorizations is studied at this part, as is the complexity of an important sub-problem: finding the least-error projections. Throughout this discussion the concepts discussed are also related to concepts in pattern mining (e.g. the Boolean rank of a matrix is the minimum number of tiles needed to cover all 1s in the data).

The second part of the tutorial concentrates on algorithms for the problems. More details will be given on few algorithms, but the main concentration is on explaining the ideas and intuitions behind -- and similarities and differences between -- the algorithms. In addition to the algorithms for the standard problems, the discussion will include methods for selecting the rank and special results with sparse matrices.

Applications of these factorizations are highlighted when appropriate, but the focus of this tutorial is not on applications. It is assumed that the participants generally know applications for discrete data mining methods or matrix factorizations.}


\vspace{-2mm}
\tutorialHeader{Mining Deep Web Repositories}
{G. Das and N. Zhang}
{13:30--16:00}{Room: Wills, G25}

%\vspace{-2mm}
\tutorialIntro{With the proliferation of online repositories (e.g., databases or document corpora) hidden behind proprietary web interfaces, e.g., keyword-/form-based search and hierarchical/graph-based browsing interfaces, efficient ways of enabling machine learning and data mining tasks over contents in such hidden repositories are of increasing importance. There are two key challenges: one on the proper understanding of interfaces, and the other on learning/mining over a properly understood interface. There are three ways to enable efficient machine learning and data mining over deep web data -- (1) crawling the deep web repository before applying conventional mining techniques, (2) sampling the deep web repository before learning/mining the retrieved samples, at the expense of additional error introduces by sampling, and (3) estimating aggregates over slices of data in the deep web repository, and then using the estimated aggregates to support machine learning or data mining tasks. In this tutorial, we focus on the fundamental developments in the field, including web interface understanding, crawling, sampling, and aggregate estimation over web repositories with various types of interfaces and containing structured or unstructured data. We also discuss the potential changes required for machine learning and data mining algorithms should one choose to use the second and third methods described above. Our goal is to encourage the audience to initiate their own research in these exciting areas.}
