=Paper=
{{Paper
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|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-972/invited3.pdf
|volume=Vol-972
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-972/invited3.pdf</pdf>
<pre>
 Data characteristics and their relation to closed
         patterns discovery algorithms

                              Engelbert Mephu Nguifo

          LIMOS, Clermont University, Blaise Pascal University and CNRS
                           Clermont-Ferrand, France
                               mephu@isima.fr

Abstract. Closed frequent patterns discovery remains a challenge in data mining.
During the last decade, different works on data mining algorithms have based their
performance evaluation on one dataset characteristic: its density (or on the contrary
its sparseness). The incoming of massive datasets in different applications, points out
the important goal to design efficient algorithms. The density measurement have shown
to be a direction to reach such goal, especially when dealing with formal context of
concept lattices. This talk will discuss this notion and describe some metrics defined
to characterize dataset density for patterns discovery purpose.


References
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   itemsets. Discrete Appl. Math. 157(13) (July 2009) 2945–2957
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   (0) (2012) –
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   itemsets on multicore: Parallelizing computations and optimizing memory accesses.
   In: HPCS. (2010) 521–528
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   16–31

</pre>