Vorlesungen, Seminare und Praktika aus dem Bereich Daten- und Informationsmanagement
Lectures, seminars and labs from the area Data and Information Management
von jsb » 22.02.14 14:57
Hallo,
hat jemand in den letzten Jahren eine Klausur in Data Mining geschrieben?
Kann sich wer erinnern, welche Themen, bzw. Aufgabenstellungen da so dran kamen?
Grüße
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jsb
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- Beiträge: 6
- Registriert: 04.08.10 14:24
- Studiengang: Informatik (B.Sc.)
- Studiert seit: SS 10
von jsb » 20.03.14 00:37
Folgende Themen waren in der ersten Klausur im WS2013/2014 dran dran (Gedächtnisprotokoll, keine Gewähr für Korrektheit oder Vollständigkeit):
KDD-Process- What is the objective of Clustering, Classification, Regression, Frequent Itemset Mining?
Decision Tree Learning- Complete a given decision tree using Gini Index
Clustering- Hierarchical Agglomerative Clustering:
Give definitions of Single / Complete / Average Link
Perform HAC with Single Link and Manhattan Distance on a given point set and draw the dendrogram - k-Median: Given a figure of a clustering (k = 2, Euclidean Distance), explain whether this is a valid k-Median Clustering or not
- DBSCAN:
Given a figure of a clustering (and DBSCAN parameters), explain whether this is a valid DBSCAN Clustering or not
Given a figure of a point cloud, determine DBSCAN parameters minPts, epsilon using the heuristic
FP-Trees- Given an initial FP-Tree, complete the FP-Tree Growth algorithm. Determine all Frequent Itemsets and draw all conditional FP-Trees.
B-Trees- Given the coordinates of some 2D points, determine their index on a 2nd order Hilbert Curve
- Given a B-Tree (M = 2), insert some keys
- Given a B-Tree (M = 4), delete some keys
Bitmap Index- Given a set of values, determine their representation in a Bitmap Index using: Simple Encoding, Range Encoding, Interval Encoding
- Formulate some queries for these three Bitmap Indices
Bayes Classifier- Give the definition of the Bayes Classifier
- Give the definition of the Naive Bayes Classifier
- Classify an object using a Naive Bayes Classifier
- Give an example where the Bayes Classifier performs better than the Naive Bayes Classifier
Prediction- Given a training set and a test set of 2D points.
- Train a constant model using the training set and determine the Mean Average Deviation using the test set
- Train a Regression Tree with one split (by just looking at the points and drawing) using the training set and determine the Mean Average Deviation using the test set
Bias / Variance- Give definitions of Bias and Variance
- Explain the relation between Bias, Variance, Overfitting
Distance Functions- Given a figure with three points, answer some questions about their L1 and L2 distances to each other
- Prove or disprove whether some given distance functions are metrics
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jsb
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- Beiträge: 6
- Registriert: 04.08.10 14:24
- Studiengang: Informatik (B.Sc.)
- Studiert seit: SS 10
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