1. komponenta

Lecture type	Total
Lectures	30
Exercises	15

* Load is given in academic hour (1 academic hour = 45 minutes)

COURSE AIMS AND OBJECTIVES: To specify and implement some algorithms for intelligent data search and data analysis.

COURSE DESCRIPTION AND SYLLABUS:
1. [1] Motivation. Examples of intelligent data analysis. Search engines.
2. [5] Vector model of data. Noise reduction. Best lover rank approximation. SVD and latent semantic indexing. Methods and applications (Internet, protein structure).
3. [4] Grouping algorithms. Spectral partition of weighted graphs. Laplace matrix and Fiedler vector. Probabilistic approaches.
4. [4] Page ranking. Hubs and authorities. Dead ends and spider traps. Markov models. Practical methods.

1. R. B. Yates, B. R. Neto: Modern Information Retrieval
2. S. Mitra, T. Acharya: Data Mining
3. M. W. Berry, Z. Drmač, E. R. Jessup: Using linear algebra for information retrieval
4. C. H. Q. Ding, H. Zha, X. He, P. Husbands, H. D. Simon: Link analysis: Hubs and Authorities on the World Wide Web
5. M. Bianchini, M. Gori, F, Scarselli: Inside PageRank

Enrollment :
Passed : Introduction to data mining

Izborni modul Modeliranje i pretraživanje baza podataka - Regular study - Applied Mathematics

Izborni modul Modeliranje i pretraživanje baza podataka - Regular study - Applied Mathematics

Izborni modul Modeliranje i pretraživanje baza podataka - Regular study - Applied Mathematics

Izborni modul Modeliranje i pretraživanje baza podataka - Regular study - Applied Mathematics