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Invited Speakers
Title: Bricolage: Data at Play
Abstract: Recently, a number of websites have emerged to enable people to
collaboratively post, visualize, curate and discuss data. These include
quantitative data visualization sites like Swivel, Many Eyes, and Data 360,
and more ontological sites like Freebase. Unlike traditional data warehouses,
these sites take the premise that communal, free-form play with data can bring
to the surface new ideas, new connections, and new kinds of social discourse
and understanding. This echoes tensions between Engineering and Bricolage as
they are conceived in 20th century philosophy, and the notion of "freeplay" as
a (or the) methodology for wrestling with imperfect and complex material.
This talk will describe some of the ideas emerging in this context, with a
focus on opportunities for data mining technologies to facilitate, inspire,
and take advantage of communal play with data.
Prof. Ian Witten, University
of Waikato, New Zealand
Title: Learning from Society
Abstract: Since its inception in the 1950s the field of machine learning has
advanced beyond recognition. It has moved from rags to riches: from a
preoccupation with stimulus-response theory -- emulating pigeons -- to an
essential tool for helping people understand and interact with the information
they need for work and play.
Search engines are the guardians of society's treasure trove of knowledge, and
in a world tainted by hype, misrepresentation and deceit, techniques of
machine learning have a crucial part to play. The field is poised to take
center stage as the Web 2.0 movement -- with its mantra of creating
applications that get better the more people use them -- accelerates. Building
upon this input from society, machine learning will supply the technologies
that intermediate between people and the information they seek, shedding a
bright light into the unfathomable "black box" that conceals the inner
workings of today's search engines.
This talk will give a brief personal synopsis of the extraordinary success
story of machine learning and illustrate how learning from society in the Web
2.0 world will provide some of the value-added functions of the librarians who
have traditionally connected users with the information they need.
Title: Tensor Decompositions and Data Mining
Abstract: Matrix decompositions such as the singular value decomposition (SVD)
are simple, well-known tools for data mining that have been used in a wide
variety of data mining applications such as text retrieval, web page ranking,
and face recognition. But matrix decompositions are restricted to two-way,
tabular data. In many cases, it is more natural to arrange data into an N-way
hyperrectangle, which is referred to as a higher-order tensor for N > 2. I will
describe tensor decompositions such as CANDECOMP/PARAFAC and Tucker. These
decompositions have been in use for several decades in psychometrics and
chemometrics and have recently become popular in signal processing, numerical
analysis, neuroscience, computer vision, and data mining. I will discuss
several examples of tensor decompositions being used for hyperlink analysis for
web search, computer vision, bibliometric analysis, cross-language document
clustering, and dynamic network traffic analysis.
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