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)
Regressionsanalyse (Linear Regression, Logisitic Regression)
Assoziationsregellernen (Aprioiri, FP-Growth)
Einführung in Deep Learning

Organisatorisches

Vorlesung:
Di, 12:15 - 13:45
Zuse
Erster Veranstaltungstag: 25.04.2017
Übung:
Do, 14:15 - 15:45
Informatik - Übungsraum II
Do, 16:15 - 17:45
Informatik - Seminarraum 3
Erster Veranstaltungstag: 04.05.2017
WueCampus-Kurs: Link zum Kurs
Anmeldung: Eine Anmeldung vor der ersten Vorlesung ist nicht nötig! Die genauen Einschreibemodalitäten werden in der ersten Vorlesungssitzung bekannt gegeben.
Prüfung SS 2017: 01.08.2017 um 12:30, Zuse (Studierende, die nicht Master Informatik studieren)
07.08.-11.08.2017 mündliche Prüfungen (Master Studierende Informatik). Genaue Termin(e) TBA
Anmeldung über sb@home
Hinweis: Ab diesem Semester werden unterschiedliche Prüfungen für Bachelor- und Masterstudierende der Informatik angeboten.

Literatur

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

[ BibTeX ] [ URL ]

@book{ester2000knowledge, author = {Ester, Martin and Sander, Jörg}, edition = 1, keywords = {database lecture:2015 discovery classification association lecture:data-mining knowledge rules clustering from:nosebrain bible lecture:essential}, publisher = {Springer Berlin Heidelberg}, title = {Knowledge Discovery in Databases: Techniken und Anwendungen}, year = 2000 }
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 ]

@techreport{crisp, author = {Chapman, Pete and Clinton, Julian and Kerber, Randy and Khabaza, Thomas and Reinartz, Thomas and Shearer, Colin and Wirth, Rudiger}, institution = {The CRISP-DM consortium}, keywords = {crisp data dm from:nosebrain guide lecture:2015 lecture:data-mining lecture:essential mining step}, month = {August}, title = {CRISP-DM 1.0 Step-by-step data mining guide}, year = 2000 }
Advances in Knowledge Discovery and Data Mining. Fayyad, Usama M.; Piatetsky-Shapiro, Gregory; Smyth, Padhraic; Uthurusamy, Ramasamy (1996).

[ BibTeX ]

@article{1996advances, editor = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic and Uthurusamy, Ramasamy}, keywords = {lecture:2015 discovery from:nosebrain mining data advances lecture:essential lecture:data-mining knowledge}, publisher = {AAAI/MIT Press}, title = {Advances in Knowledge Discovery and Data Mining}, year = 1996 }

Weitere Literatur zur Vorlesung

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

[ BibTeX ] [ URL ]

@misc{platt1998sequential, abstract = {This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop. The amount of memory required for SMO is linear in...}, author = {Platt, J.}, keywords = {lecture:data-mining optimization smo lecture:2018 svm from:nosebrain}, title = {Sequential minimal optimization: A fast algorithm for training support vector machines}, year = 1998 }
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 ]

@article{mou2015endtoend, author = {Mou, Lili and Men, Rui and Li, Ge and Zhang, Lu and Jin, Zhi}, journal = {CoRR}, keywords = {lecture:data-mining code deep lecture:2018 learning from:nosebrain generation}, title = {On End-to-End Program Generation from User Intention by Deep Neural Networks.}, volume = {abs/1510.07211}, year = 2015 }
Mining Frequent Patterns without Candidate Generation. Han, Jiawei; Pei, Jian; Yin, Yiwen W. Chen, Naughton, J. F., Bernstein, P. A. (reds.) (2000). 1-12.

[ BibTeX ] [ URL ]

@inproceedings{han2000mining, author = {Han, Jiawei and Pei, Jian and Yin, Yiwen}, booktitle = {SIGMOD Conference}, crossref = {conf/sigmod/2000}, editor = {Chen, Weidong and Naughton, Jeffrey F. and Bernstein, Philip A.}, keywords = {lecture:data-mining mining pattern fp tree frequent lecture:2014 from:nosebrain}, note = {SIGMOD Record 29(2), June 2000}, pages = {1-12}, publisher = {ACM}, title = {Mining Frequent Patterns without Candidate Generation}, year = 2000 }
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 ]

@article{dempster1977maximum, author = {Dempster, A. P. and Laird, N. M. and Rubin, D. B.}, journal = {Journal of the Royal Statistical Society: Series B}, keywords = {lecture:data-mining lecture:2018 em from:nosebrain}, pages = {1-38}, title = {Maximum likelihood from incomplete data via the {EM} algorithm}, volume = 39, year = 1977 }
Experiments with a New Boosting Algorithm. Freund, Yoav; Schapire, Robert E. (1996). 148-156.

[ BibTeX ] [ URL ]

@inproceedings{freund1996experiments, author = {Freund, Yoav and Schapire, Robert E.}, booktitle = {International Conference on Machine Learning}, keywords = {lecture:data-mining adaboost lecture:2015 from:nosebrain}, pages = {148-156}, title = {Experiments with a New Boosting Algorithm}, year = 1996 }
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.

[ BibTeX ] [ URL ]

@article{dhar2013science, abstract = {Big data promises automated actionable knowledge creation and predictive models for use by both humans and computers.}, address = {New York, NY, USA}, author = {Dhar, Vasant}, journal = {Commun. ACM}, keywords = {science lecture:data-mining data lecture:2018 from:nosebrain}, month = {dec}, number = 12, pages = {64--73}, publisher = {ACM}, title = {Data Science and Prediction}, volume = 56, year = 2013 }
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 ]

@article{provost2013science, author = {Provost, Foster and Fawcett, Tom}, journal = {Big Data}, keywords = {science lecture:data-mining data lecture:2018 from:nosebrain}, month = {mar}, number = 1, pages = {51--59}, publisher = {Mary Ann Liebert Inc}, title = {Data Science and its Relationship to Big Data and Data-Driven Decision Making}, volume = 1, year = 2013 }
Clustering by means of medoids. Kaufman, Leonard; Rousseeuw, Peter J. (I. : D. Y; editor,, reds.) (1987). 405–416.

[ BibTeX ]

@misc{kaufmanl1987clustering, author = {Kaufman, Leonard and Rousseeuw, Peter J.}, editor = {Y, In: Dodge and editor}, keywords = {lecture:data-mining kmediods clustering pam from:nosebrain}, pages = {405–416}, publisher = {North Holland / Elsevier}, title = {Clustering by means of medoids}, year = 1987 }
Bagging, Boosting, and C4.5. Quinlan, J. Ross W. J. Clancey, Weld, D. S. (reds.) (1996). 725-730.

[ BibTeX ] [ URL ]

@inproceedings{quinlan1996bagging, author = {Quinlan, J. Ross}, booktitle = {AAAI/IAAI, Vol. 1}, crossref = {conf/aaai/1996-1}, editor = {Clancey, William J. and Weld, Daniel S.}, keywords = {lecture:data-mining comparison boosting bagging c4.5 lecture:2014 from:nosebrain}, pages = {725-730}, publisher = {AAAI Press / The MIT Press}, title = {Bagging, Boosting, and C4.5}, year = 1996 }
Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. Agrawal, Rakesh; Gehrke, Johannes; Gunopulos, Dimitrios; Raghavan, Prabhakar (1998). 94--105.

[ BibTeX ]

@inproceedings{agrawal-98, author = {Agrawal, Rakesh and Gehrke, Johannes and Gunopulos, Dimitrios and Raghavan, Prabhakar}, booktitle = {Proceedings of the ACM SIGMOD Int'l Conference on Management of Data, Seattle, Washington}, keywords = {clusterin lecture:data-mining subspace clique lecture:2014 from:nosebrain}, month = {June}, pages = {94--105}, publisher = {ACM Press}, title = {Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications}, year = 1998 }
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 ]

@inproceedings{ester1996densitybased, author = {Ester, Martin and Kriegel, Hans-Peter and Sander, Jörg and Xu, Xiaowei}, booktitle = {Proc. of 2nd International Conference on Knowledge Discovery and}, keywords = {based lecture:data-mining dbscan clustering density noise lecture:2014 from:nosebrain}, pages = {226-231}, title = {A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise}, year = 1996 }