II Semester M.E. (Bio-Informatics) Degree Examination, July 2014 (2K13 Scheme) BI 22 : DATA MINING IN BIOINFORMATICS Time : 3 Hours
Max. Marks : 100
Instruction : Answer any five questions from the following. 1. a) Write about expectations and Covariances and explain Bayesian theorem in detail. 10 b) What is meant by boot straping, explain in detail.
5
c) Explain Gaussian distribution with neat diagram.
5
2. a) Explain, information theory with neat diagrams and examples and also write a note on relative entropy. 10 b) Explain Bernoulli distribution and sufficient statistics. 3. a) Write about evidence approximation for evaluating evidence functions. b) Explain in detail about Bias-Variance decomposition.
10 10 10
4. a) Differentiate between Generative models and discriminative models.
20
5. a) Write about Neural network training for parameter optimization and gradient descent optimization.
10
b) Explain Bayesian networks and Markov random fields. 6. a) Explain combined models with examples. b) Write briefly about maximum margin classifiers. 7. a) Differentiate between SVM and RVM.
10 10 10 10
b) Write about K-mean clustering with neat diagram.
10
8. a) Explain Principle Component Analysis (PCA) in detail.
10
b) Explain Non-linear latent variable models for independent component analysis. ———————
10. b) Explain Non-linear latent variable models for independent component analysis. 10. âââââââ. Page 1. DATA MINING IN BIOINFORMATICS.pdf.
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storage or memory. ... Visualization of data patterns, considerably 3D visual image, represents one of the foremost ... Big Data Characteristics: Hace Theorem.
1 (a) Describe three challenges to data mining regarding data mining methodology and user interaction issues. (b) Draw and explain the three-tier architecture ...
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iii) Spatial data mining. iv) Text mining. âââââââ. Page 2 of 2. DATA MINING & WAREHOUSING.pdf. DATA MINING & WAREHOUSING.pdf. Open. Extract.
comings and in incorporating possible improvements. Traditional data analysis in e-learning is hypothesis or assumption driven (Gaudioso & Talavera, 2006) in ...
