Grades

• First draft for final grades
• Draft for final grades after exam review
• Final grades before oral examinations (students marked with Oral should contact us)

Exams

• 14/01/2019 Exam A and Exam B
• 14/01/2019 Exam solutions
• 14/01/2019 Exam grades (answers)
• Exam 28/01/2019
• NEW 30/01/2019 2nd Exam grades
  

Office Support Schedule

Office hours before second exam to be released soon
NEW Prof. Cláudia Antunes = Exams Season

• [new] second season: Alameda: 25th January at 11:00 @ Informática building II, office 1

• [new] second season: Tagus: 25th January at 09:00 @ room 2N3.15

Prof. Rui Henriques = Exams season

• Alameda: 7th and 11th January at 11:00 @ Informática building III, room 0.09
• Tagus: 7th and 11th January at 14:30 @ office 2N1.25
• [new] second season: Alameda 22nd January at 11:00 @ Informática building III, room 0.09
  
• [new] second season: Tagus 23rd January at 11:00 @ office 2N1.25
  

Bibliography Support

• Mohammed J. Zaki, Wagner Meira, Jr. Data Mining and Analysis: Fundamental Concepts and Algorithms. 2014 Cambridge University Press 
• Jiawei Han, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011
• Top 10 algorithms in data mining
  
• Naren Ramakrishnan. C4.5
• Dan Steinberg. CART: Classifaction and Regression Trees

Exercises

• NEW Exercises Book Part I by Cláudia Antunes and Rui Henriques
• NEW Exercises Book Part II v4 by Rui and Cláudia Antunes (stable version uploaded 24/12)
• • Changes in version 5 (Book Part II v5 uploaded 7/Jan)
  • • FSelection 2.1:(5), Clustering 2.1:(3), Regression 1.2:(1),
      
    • Biclustering 4:(2) and x8=<0,1,2,0.5>, Forecasting 1:(3), 3:(4)
  • Important note on temporal data mining question 1
  • • distance matrix using traditional DTW (the one required for exam) is [[0,22,3,11],[22,0,11,8],[3,11,0,9],[11,8,9,0]]
      
    • the R-variant of DTW (not required for exam) was considered here, to answer 1.1 and 1.2 assume distance matrix to be [[0,8,3,5],[8,0,5,6],[3,5,0,5],[5,6,5,0]] (where [[d(x1,x1),d(x1,x2)...]...])
      
  • Answer key for version 4:
  • • FSelection: 1.1:(2), 1.2:(4), 2.1:the incorrect is (5), 2.2:(2&4), 3.1:(3), 3.2:(1&3&4)
      
    • Clustering: 1.1:(4), 1.2:(2), 1.3:(2), 2.1:(1&3), 2.2:(3), 3:(3)
      
    • PCA: 1:(2), 2:(3), 3:(4), 4:(4)
      
    • Regression: 1.1.b:(3), 1.2:(1&3), 1.3:(6), 2.1:(3), 2.2:(3), 3:(4)
      
    • Biclustering: 1:(4), 2:(3&4&5&7), 3:(3), 4:(1&2) and x8=<0,1,2,0.5>
    • Sequential pattern mining: 1:(2), 2:(3)
      
    • Time series forecasting: 1:(2&3), 2:(5), 3:(1&4)
      
    • Time series representations: 1.1:(3&4), 1.2:(5), 1.3(4), 2.1:(4), 3:(1&2&4)
      
    • Temporal data mining: 1.1:(1&3), 1.2:(2), 2.1:(3), 2.2:(all)
      

Slides

• T1a - Introduction and organization
• T1b - Data Science (Han 1, Zaki 1)
• T2 - Data Exploration (Han 2, Zaki 2 & 3)
• T3 - Classification: Analogizers (Han 9.5 & 9.3, Zaki 18.3  & 21.1)
• T4 - Classification: Bayesian (Han 8.3 & 8.5, Zaki 18)
• T5 - Evaluation, Data Balancing (Han 12, Zaki 22)
• T6,7,8 - Classification: Decision Trees (Han 8.2, Zaki 19)
• T9 - Classification: Ensemble Methods (Han 8.6)
• T10 - Classification: other approaches (Han 9.2)
• T11 - Pattern Mining (Han 6 & 7, Zaki 8 & 9 & 10 & 12)
  
• T12 - Clustering (Han 10 & 11.1, Zaki 13-17)
• T13 - Dimensionality and Data Reduction (Han 3.4 & 3.5)
  
• T14 - Data Transformation (Zaki 6 & 7)
• T15,16 - Social Netowrk Analysis
• T17 - Regression (not covered in bibliography, Gelman&Hill part 1A)
• T18 - Biclustering (not covered in bibliography, Madeira&Oliveira, Henriques&Madeira)
• T19 - Time Series Description and Forecasting [p.46 updated] (not covered in bibliography, Bisgaard&Kulahci)
  
• T20a - Time Series Representations (not covered in bibliography, Lin&Keogh)
  
• T20b - Fourier and Wavelet Transform (not covered in bibliography, Boggess&Narcowich 3 & 4 & 5)
  
• T21 - Temporal Data Mining (not covered in bibliography, Esling&Agon, Mörchen)
• T22 - Outlier Analysis (not covered in bibliography, Aggarwal)
  
• T23 - Biomedical Data Analysis (covers principles from all lectures)
• T24 - Complex Data Mining (not covered in bibliography, Atluri&Karpatne&Kumar and Dzeroski)
• T25 - Notes on Big Data (not covered in bibliography, Zaki and Aggarwal)
  
• T26 - Closing remarks

Exam preparation

• T12 - slides 4,12-36,38-69,72,80,84-95,101-103,109-111
• T13 - slides 4-31,38-39
• T14 - slides 5-25,29-34,43-44
• T17 - slides 4-29,34-49
• T18 - slides 4-36,47-53
• T19 - slides 3-21,31-28,42,44,46,51-53,57-58,61,64,66,69,72,76,83-85
• T20a - slides 3-5,8-32
• T20b - slides 3-10,16-21,23-29,31,33
• T21 - slides 4-18,20-35,37,38,40,48-51
• T22 - slides 3-13,15,20,22
• T23 - slides 10,46,62,98,103
• T24 - slides 4,8-10,18,21-23,25,34,54,63,66-67,71-72
• T25 - slides 5,7,11-14,20,22
  
• Remaining lectures: full PPT as study reference
• Final exam will not ask for definitions or a memory-based listing of slide contents