https://sdq.kastel.kit.edu/index.php?title=A_comparative_study_of_subgroup_discovery_methods&feed=atom&action=historyA comparative study of subgroup discovery methods - Versionsgeschichte2024-03-29T10:52:36ZVersionsgeschichte dieser Seite in SDQ-InstitutsseminarMediaWiki 1.39.6https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=A_comparative_study_of_subgroup_discovery_methods&diff=1567&oldid=prevNv3463 am 17. Februar 2021 um 08:56 Uhr2021-02-17T08:56:28Z<p></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
<col class="diff-marker" />
<col class="diff-content" />
<col class="diff-marker" />
<col class="diff-content" />
<tr class="diff-title" lang="de">
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Nächstältere Version</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Version vom 17. Februar 2021, 09:56 Uhr</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l4">Zeile 4:</td>
<td colspan="2" class="diff-lineno">Zeile 4:</td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|vortragstyp=Bachelorarbeit</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|vortragstyp=Bachelorarbeit</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|betreuer=Vadim Arzamasov</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|betreuer=Vadim Arzamasov</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|termin=Institutsseminar/2021-02-19</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>|termin=Institutsseminar/2021-02-19 <ins style="font-weight: bold; text-decoration: none;">Zusatztermin</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|kurzfassung=Subgroup discovery is a data mining technique that is used to extract interesting relationships in a dataset related to to a target variable. These relationships are described in the form of rules. Multiple SD techniques have been developed over the years. This thesis establishes a comparative study between a number of these techniques in order to identify the state-of-the-art methods. It also analyses the effects discretization has on them as a preprocessing step . Furthermore, it investigates the effect of hyperparameter optimization on these methods. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|kurzfassung=Subgroup discovery is a data mining technique that is used to extract interesting relationships in a dataset related to to a target variable. These relationships are described in the form of rules. Multiple SD techniques have been developed over the years. This thesis establishes a comparative study between a number of these techniques in order to identify the state-of-the-art methods. It also analyses the effects discretization has on them as a preprocessing step . Furthermore, it investigates the effect of hyperparameter optimization on these methods. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Our analysis showed that PRIM, DSSD, Best Interval and FSSD outperformed the other subgroup discovery methods evaluated in this study and are to be considered state-of-the-art . It also shows that discretization offers an efficiency improvement on methods that do not employ internal discretization. It has a negative impact on the quality of subgroups generated by methods that perform it internally. The results finally demonstrates that Apriori-SD and SD-Algorithm were the most positively affected by the hyperparameter optimization.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Our analysis showed that PRIM, DSSD, Best Interval and FSSD outperformed the other subgroup discovery methods evaluated in this study and are to be considered state-of-the-art . It also shows that discretization offers an efficiency improvement on methods that do not employ internal discretization. It has a negative impact on the quality of subgroups generated by methods that perform it internally. The results finally demonstrates that Apriori-SD and SD-Algorithm were the most positively affected by the hyperparameter optimization.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>}}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>}}</div></td></tr>
</table>Nv3463https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=A_comparative_study_of_subgroup_discovery_methods&diff=1565&oldid=prevUwejw am 13. Februar 2021 um 10:17 Uhr2021-02-13T10:17:17Z<p></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
<col class="diff-marker" />
<col class="diff-content" />
<col class="diff-marker" />
<col class="diff-content" />
<tr class="diff-title" lang="de">
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Nächstältere Version</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Version vom 13. Februar 2021, 11:17 Uhr</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l5">Zeile 5:</td>
<td colspan="2" class="diff-lineno">Zeile 5:</td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|betreuer=Vadim Arzamasov</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|betreuer=Vadim Arzamasov</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|termin=Institutsseminar/2021-02-19</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|termin=Institutsseminar/2021-02-19</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|kurzfassung=<del style="font-weight: bold; text-decoration: none;">TBD</del></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>|kurzfassung=<ins style="font-weight: bold; text-decoration: none;">Subgroup discovery is a data mining technique that is used to extract interesting relationships in a dataset related to to a target variable. These relationships are described in the form of rules. Multiple SD techniques have been developed over the years. This thesis establishes a comparative study between a number of these techniques in order to identify the state-of-the-art methods. It also analyses the effects discretization has on them as a preprocessing step . Furthermore, it investigates the effect of hyperparameter optimization on these methods. </ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Our analysis showed that PRIM, DSSD, Best Interval and FSSD outperformed the other subgroup discovery methods evaluated in this study and are to be considered state-of-the-art . It also shows that discretization offers an efficiency improvement on methods that do not employ internal discretization. It has a negative impact on the quality of subgroups generated by methods that perform it internally. The results finally demonstrates that Apriori-SD and SD-Algorithm were the most positively affected by the hyperparameter optimization.</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>}}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>}}</div></td></tr>
</table>Uwejwhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=A_comparative_study_of_subgroup_discovery_methods&diff=1546&oldid=prevUwejw: Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Mohamed Amine Chalghoum |email=uwejw@student.kit.edu |vortragstyp=Bachelorarbeit |betreuer=Vadim Arzamasov |termin=Institutsseminar/202…“2021-01-30T20:57:30Z<p>Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Mohamed Amine Chalghoum |email=uwejw@student.kit.edu |vortragstyp=Bachelorarbeit |betreuer=Vadim Arzamasov |termin=Institutsseminar/202…“</p>
<p><b>Neue Seite</b></p><div>{{Vortrag<br />
|vortragender=Mohamed Amine Chalghoum<br />
|email=uwejw@student.kit.edu<br />
|vortragstyp=Bachelorarbeit<br />
|betreuer=Vadim Arzamasov<br />
|termin=Institutsseminar/2021-02-19<br />
|kurzfassung=TBD<br />
}}</div>Uwejw