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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:
13.07.2009
Vorlesungsaufzeichnungen 07.04.2008 01:14:23 244 Data and Web Mining Introduction, What is in the Web?, Be cautious!, Maths, Matlab, Random Graphs 08.04.2008 01:12:00 29 Random Graphs Maths, Random Graphs, Matlab, Resumee, Data and Web Mining, Link Analysis 14.04.2008 01:17:44 272 Link Structure The importance of Link Structure, Judge the Link Structure, Maths, Hits, Matlab, PageRank 15.04.2008 01:13:11 393 PageRank PageRank, Matlab, SALSA, Google and co. 21.04.2008 01:16:51 285 Google and co Google and co, How to fool Google?, Clustering, What is the goal of clustering? 28.04.2008 01:16:45 236 Spectral clustering k-means, Matlab, Maths 29.04.2008 47:18 146 Spectral clustering II k-means, Maths, Matlab, How to turn Data into a Graph 29.04.2008 25:41 159 Spectral clustering II How to turn Data into a Graph, Affinity propagation 05.05.2008 01:08:27 174 Maths Maths, Affinity propagation, Matlab, Relational clustering 19.05.2008 01:22:21 173 Relational clustering Relational clustering, Matlab, Graph to (dis-)similarities, Evaluation, Visualization, What is the goal of visualization? 20.05.2008 01:28:45 222 PCA Principal component analysis, Independent component analysis (ICA) 26.05.2008 01:16:34 138 ICA Independent component analysis, Fisher linear discriminance analysis (LDA), Multidimensional scaling (MDS) 02.06.2008 26:37 138 MDS Metrisches Multidimensional scaling 03.06.2008 01:25:08 121 LLE Multidimensional scaling, Isomap, Locally linear embedding, Zusammenfassung 16.06.2008 01:29:52 228 Mining A priori algorithm, Determine large itemsets, Determine antecedent from large itemsets, Weka, Decision trees 23.06.2008 37:44 6 Decision trees (Teil 1) Desicion trees, Information gain, Best attribute, Universal approximators, ID3, Overfitting 23.06.2008 31:52 1 Decision trees (Teil 2) Desicion trees, Lazy learning, k-nearest neighbor classifier, Inductive bias oh k-NN 24.06.2008 01:22:35 7 Lazy learning Weighted k-NN regression, Naive Bayes, Bayes rule, Maximum likelihood classifier, Maximum a posteriori hypothesis, Collaborative filtering 30.06.2008 01:21:13 143 Collaborative filtering Collaborative filtering, Singular value decomposition, Text preprocessing, Bag of words representation, tf x idf weighting, tfc weighting 07.07.2008 01:11:52 3 Bag of words representation Bag of words representation, Document frequency thresholding, SVD, Johnson-Lindenstrauss lemma, String kernels, Spectrum kernel 08.07.2008 01:20:03 6 String kernels String kernels, How to compute the String kernel?, Compression distance, Kolmogorov complexity, Normalized information distance, Streaming data 14.07.2008 01:02:43 4 Patch neural gas Patch neural gas, Streaming data, Patch relational neural gas, Frequent itemsets, Resumee
