Data Mining

Die Vorlesung gibt einen Überblick über die Wissensgewinnung aus (strukturierten) Daten. Dazu gehören unter anderem:

Vorverabeitungstechniken
OLAP-Analyse & Data-Warehousing
Clustering (k-means, k-medoids, DBSCAN, OPTICS)
Klassifikation (k-Nearest-Neighbor, Bayes, Entscheidungsbaum, Support Vector Machine; Bagging, Boosting, z. B. Random Forest, AdaBoost)
Assoziationsregellernen (Aprioiri, FP-Growth)
Subgruppenentdeckung

Organisatorisches

Vorlesung:
Di, 12:15 - 13:45
Informatik - Übungsraum II
Erster Veranstaltungstag: 14.04.2015
Übung:
Do, 14:15 - 15:45
Informatik - Übungsraum II
Erster Veranstaltungstag: 23.04.2015
WueCampus-Kurs:
Kurs
Anmeldung: Eine Anmeldung vor der ersten Vorlesung ist nicht nötig! Die genauen Einschreibemodalitäten werden in der ersten Vorlesungssitzung bekannt gegeben.
Prüfung SS 2015: 22.07.2015 10:15
Prüfung WS 2015/16: 05.10.2015 10:15 (schriftlich) oder 05./06.10.2015 mündlich, je nach Teilnehmerzahl. Anmeldung über sb@home

Literatur

Knowledge Discovery in Databases: Techniken und Anwendungen Ester, Martin; Sander, Jörg (2000). (1st ed.) Springer Berlin Heidelberg.

[ BibTeX ] [ URL ]

CRISP-DM 1.0 Step-by-step data mining guide Chapman, Pete; Clinton, Julian; Kerber, Randy; Khabaza, Thomas; Reinartz, Thomas; Shearer, Colin; Wirth, Rudiger (2000).

[ BibTeX ] [ URL ]

Advances in Knowledge Discovery and Data Mining. Fayyad, Usama M.; Piatetsky-Shapiro, Gregory; Smyth, Padhraic; Uthurusamy, Ramasamy (1996).

[ BibTeX ]

Weitere Literatur zur Vorlesung

Sequential minimal optimization: A fast algorithm for training support vector machines. Platt, J. (1998).

[ BibTeX ] [ URL ]

OPTICS: Ordering Points To Identify the Clustering Structure. Ankerst, Mihael; Breunig, Markus M.; peter Kriegel, Hans; Sander, Jörg (1999). 49–60.

[ BibTeX ]

                                                                @inproceedings{ankerst1999optics,
  abstract = {Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, for many real-data sets there does not even exist a global parameter setting for which the result of the clustering algorithm describes the intrinsic clustering structure accurately. We introduce a new algorithm for the purpose of cluster analysis which does not produce a clustering of a data set explicitly; but instead creates an augmented ordering of the database representing its density-based clustering structure. This cluster-ordering contains information which is equivalent to the density-based clusterings corresponding to a broad range of parameter settings. It is a versatile basis for both automatic and interactive cluster analysis. We show how to automatically and efficiently extract not only ‘traditional ’ clustering information (e.g. representative points, arbitrary shaped clusters), but also the intrinsic clustering structure. For medium sized data sets, the cluster-ordering can be represented graphically and for very large data sets, we introduce an appropriate visualization technique. Both are suitable for interactive exploration of the intrinsic clustering structure offering additional insights into the distribution and correlation of the data.},
  author = {Ankerst, Mihael and Breunig, Markus M. and peter Kriegel, Hans and Sander, Jörg},
  keywords = {lecture:data-mining clustering lecture:2014 from:nosebrain optics},
  pages = {49--60},
  publisher = {ACM Press},
  title = {OPTICS: Ordering Points To Identify the Clustering Structure},
  year = 1999
}

On End-to-End Program Generation from User Intention by Deep Neural Networks. Mou, Lili; Men, Rui; Li, Ge; Zhang, Lu; Jin, Zhi in CoRR (2015). abs/1510.07211

[ BibTeX ] [ URL ]

Mining Frequent Patterns without Candidate Generation. Han, Jiawei; Pei, Jian; Yin, Yiwen W. Chen, J. F. Naughton, P. A. Bernstein (eds.) (2000). 1–12.

[ BibTeX ] [ URL ]

Maximum likelihood from incomplete data via the EM algorithm. Dempster, A. P.; Laird, N. M.; Rubin, D. B. in Journal of the Royal Statistical Society: Series B (1977). 39 1–38.

[ BibTeX ] [ URL ]

Experiments with a New Boosting Algorithm. Freund, Yoav; Schapire, Robert E. (1996). 148–156.

[ BibTeX ] [ URL ]

Experimental evidence of massive-scale emotional contagion through social networks. Kramer, Adam D. I.; Guillory, Jamie E.; Hancock, Jeffrey T. in Proceedings of the National Academy of Sciences (2014). 111(24) 8788–8790.

[ BibTeX ] [ URL ]

                                                                @article{kramer2014experimental,
  abstract = {Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others’ positive experiences constitutes a positive experience for people.},
  author = {Kramer, Adam D. I. and Guillory, Jamie E. and Hancock, Jeffrey T.},
  journal = {Proceedings of the National Academy of Sciences},
  keywords = {lecture:data-mining social emotional facebook lecture:2015 network from:nosebrain contagion},
  number = 24,
  pages = {8788-8790},
  title = {Experimental evidence of massive-scale emotional contagion through social networks},
  volume = 111,
  year = 2014
}

Data Science and Prediction. Dhar, Vasant in Commun. ACM (2013). 56(12) 64–73.

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Data Science and its Relationship to Big Data and Data-Driven Decision Making. Provost, Foster; Fawcett, Tom in Big Data (2013). 1(1) 51–59.

[ BibTeX ]

Clustering by means of medoids. Kaufman, Leonard; Rousseeuw, Peter J. (I. D. Y; editor, eds.) (1987). 405–416.

[ BibTeX ]

Bagging, Boosting, and C4.5. Quinlan, J. Ross W. J. Clancey, D. S. Weld (eds.) (1996). 725–730.

[ BibTeX ] [ URL ]

Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. Agrawal, Rakesh; Gehrke, Johannes; Gunopulos, Dimitrios; Raghavan, Prabhakar (1998). 94–105.

[ BibTeX ]

A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Ester, Martin; Kriegel, Hans-Peter; Sander, Jörg; Xu, Xiaowei (1996). 226–231.

[ BibTeX ]

Data Mining

Organisatorisches

Literatur

Weitere Literatur zur Vorlesung

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