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Data and Web Mining

von Prof. Dr. Barbara Hammer

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Beschreibung

Die Vorlesung behandelt wichtige Techniken des Data und Web-Minings. Es werden grundlegende Algorithmen behandelt und in praktikschen Übungen umgesetzt. Einzelne Themen sind:
überwachtes Lernen: Entscheidungsbäume, Bayessche Inferenz, Diskriminanzanalyse, Graphische Modelle
unüberwachtes Lernen: a priori Algorithmus, Generative Modelle, Hidden Markov Modelle, Clustering
Textanalyse: Singulärwertzerlegung, Random Projection, Indexing, Lexikalische Analyse, Coclustering
Linkanalyse: Pagerank, Hits
Human-Computer-Interaction: Modelling Browsing Behavior, Collaborative Filtering

4.2008

Vorlesungsaufzeichnungen

07.04.200801:14:2317
Data and Web Mining
Introduction, What is in the Web?, Be cautious!, Maths, Matlab, Random Graphs
08.04.200801:12:000
Random Graphs
Maths, Random Graphs, Matlab, Resumee, Data and Web Mining, Link Analysis
14.04.200801:17:44262
Link Structure
The importance of Link Structure, Judge the Link Structure, Maths, Hits, Matlab, PageRank
15.04.200801:13:11384
PageRank
PageRank, Matlab, SALSA, Google and co.
21.04.200801:16:51266
Google and co
Google and co, How to fool Google?, Clustering, What is the goal of clustering?
28.04.200801:16:45226
Spectral clustering
k-means, Matlab, Maths
29.04.200847:18143
Spectral clustering II
k-means, Maths, Matlab, How to turn Data into a Graph
29.04.200825:41156
Spectral clustering II
How to turn Data into a Graph, Affinity propagation
05.05.200801:08:27165
Maths
Maths, Affinity propagation, Matlab, Relational clustering
19.05.200801:22:21167
Relational clustering
Relational clustering, Matlab, Graph to (dis-)similarities, Evaluation, Visualization, What is the goal of visualization?
20.05.200801:28:45198
PCA
Principal component analysis, Independent component analysis (ICA)
26.05.200801:16:34118
ICA
Independent component analysis, Fisher linear discriminance analysis (LDA), Multidimensional scaling (MDS)
02.06.200826:37123
MDS
Metrisches Multidimensional scaling
03.06.200801:25:08106
LLE
Multidimensional scaling, Isomap, Locally linear embedding, Zusammenfassung
16.06.200801:29:52201
Mining
A priori algorithm, Determine large itemsets, Determine antecedent from large itemsets, Weka, Decision trees
23.06.200837:43147
Decision trees (Teil 1)
Desicion trees, Information gain, Best attribute, Universal approximators, ID3, Overfitting
23.06.200831:52125
Decision trees (Teil 2)
Desicion trees, Lazy learning, k-nearest neighbor classifier, Inductive bias oh k-NN
24.06.200801:22:35147
Lazy learning
Weighted k-NN regression, Naive Bayes, Bayes rule, Maximum likelihood classifier, Maximum a posteriori hypothesis, Collaborative filtering
30.06.200801:21:13125
Collaborative filtering
Collaborative filtering, Singular value decomposition, Text preprocessing, Bag of words representation, tf x idf weighting, tfc weighting
07.07.200801:11:52128
Bag of words representation
Bag of words representation, Document frequency thresholding, SVD, Johnson-Lindenstrauss lemma, String kernels, Spectrum kernel
08.07.200801:20:03119
String kernels
String kernels, How to compute the String kernel?, Compression distance, Kolmogorov complexity, Normalized information distance, Streaming data
14.07.200822:02117
Patch neural gas
Patch neural gas, Streaming data, Patch relational neural gas, Frequent itemsets, Resumee
14.07.200840:40125
Patch neural gas
Patch neural gas, Streaming data, Patch relational neural gas, Frequent itemsets, Resumee