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|title=None
|pdfUrl=https://ceur-ws.org/Vol-2436/invited_3.pdf
|volume=Vol-2436
|dblpUrl=https://dblp.org/rec/conf/sdm/Goethals19
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<pdf width="1500px">https://ceur-ws.org/Vol-2436/invited_3.pdf</pdf>
<pre>
                            Lessons learned from the FIMI workshops

                                                Bart Goethals ∗
                                                 May 4, 2019



Abstract of Invited Presentation
In 2003 and 2004, Mohammed Zaki and myself organised the Frequent Itemset Mining Implementations (FIMI)
workshops in an attempt to characterize and understand the algorithmic performance space of frequent itemset
mining algorithms.
    In the years before, a huge number of algorithms had been developed in order to efficiently solve the frequent
itemset mining (FIM) problem and several new algorithms were shown by their authors to run faster than
previously existing algorithms. Unfortunately, the performance behavior of several of these algorithms was not
always as was claimed by its authors when tested on some different datasets. Our goal was to understand precisely
why and under what conditions one algorithm would outperform another.
    In this talk, I will present some concrete examples of experimental evaluations that motivated the FIMI
workshops, discuss the workshops results and whether we reached our goals, and my personal view on the
evaluation of data mining methods.




  ∗ University of Antwerp

</pre>