The aim of this project is to discover the topics of scientific papers published by researches of DEMS (Department of Economics, Management and Statistic) for the University of Milano-Bicocca.

1. DATA
SCIENCE LAB
PROJECT
Master Degree: Data Science
Accomplished by:
A. Portaluppi & L. Ravazzi &
M. Spandri
A.A. 2019-2020
1

2. INTRODUCTION
DATA
SET
DEMS Publications
(Dipartimento di
Economia, Metodi
Quantitativi e Strategie
di Impresa).
Find topics studied
by DEMS universitary
researcher.
Multidimensional
Scaling techniques
and Cluster
Analysis.
2
PURPOSES TOOLS

3. DATA
MANAGEMENT:
1. Exploration
2. Preprocessing
3. Data Cleaning
4. NLP
MULTI-
DIMENSIONAL
SCALING:
1. Common
Multidimension
al Scaling
2. Metric Scaling
3. Sammon
Mapping
CLUSTER
ANALYSIS:
Prototype-Based:
Fuzzy Algorithm
DATA
VISUALIZATION:
RShiny
Application
STEP BY STEP
3

4. DATA
MANAGEMENT
DATA
VISUALIZATION
CLUSTER
ANALYSIS
MULTIDIMENSIONAL
SCALING
ID TITLE JOURNAL
ABSTRAC
T
ABSTRAC
T_ENG
KEYWOR
DS
KEYWOR
DS_ENG
235 … … …
ID DEMS_AUTHORS
235 …
235 …
235 …
…
HOW TO MANAGE THE DATA SETS?
4

5. DATA
MANAGEMENT
DATA
VISUALIZATION
CLUSTER
ANALYSIS
MULTIDIMENSIONAL
SCALING
HOW TO CHOOSE
RECORDS?
30% DOCUMENTS LEFT
5
JOURNAL ARTICLES
WRITTEN BY
ASSISTANT
PROFESSORS ETC.
ENGLISH
LANGUAGE

6. DATA
MANAGEMENT
DATA
VISUALIZATION
CLUSTER
ANALYSIS
MULTIDIMENSIONAL
SCALING
What does it means in English?
Perfect! There is a field
which specifies the
language.
The language of an article is
the language of the abstract
(textcat function).
6

7. DATA
MANAGEMENT
DATA
VISUALIZATION
CLUSTER
ANALYSIS
MULTIDIMENSIONAL
SCALING
NATURAL LANGUAGE PROCESSING
Start with mixed texts
(title, abstract,
keywords and journal)
Bag of
words
1. Drop out punctuation,
stop-words, non-letter
character
2. All in lower case
3. Stemming process
1 2 3 4
Compute
tf_idf
7

8. DATA
MANAGEMENT
DATA
VISUALIZATION
CLUSTER
ANALYSIS
MULTIDIMENSIONAL
SCALING
MULTIDIMENSIONAL SCALING
WHAT?
A function to
project data from
a N-dimensional
space to 2 or 3
dimensions
WHY?
• Graphical
approach
(Clustering)
• Increase
Interpretability
HOW?
• Metric
• Non Metric
8

9. DATA
MANAGEMENT
DATA
VISUALIZATION
CLUSTER
ANALYSIS
MULTIDIMENSIONAL
SCALING
Table with text and the
number of terms into the
bag of words.
Choose a proximity measures.
Apply the desidered
technique.
9

10. DATA
MANAGEMENT
DATA
VISUALIZATION
CLUSTER
ANALYSIS
MULTIDIMENSIONAL
SCALING
1. Common Multidimensional Scaling
(Euclidean distance)
2. Metric Scaling
3. Sammon Mapping (Manhattan
distance)
We applied three
techniques:
and will describe only the last
one due to the good results.
10

11. DATA
MANAGEMENT
DATA
VISUALIZATION
CLUSTER
ANALYSIS
MULTIDIMENSIONAL
SCALING
SAMMON MAPPING
Minimize Sammon Stress:
where is the distance
between the i-th and j-th
observation in the initial space,
while refers to the final
space.
For metric
and non
metric data
Non-linear
trasformation
approach
(different from
PCA)
11

12. DATA
MANAGEMENT
DATA
VISUALIZATION
CLUSTER
ANALYSIS
MULTIDIMENSIONAL
SCALING
Since an article can touch
different topics, clustering must
be of fuzzy type.
CLUSTERING
Labels of clusters rely on the
fifteen words most frequent
in the bag of words.
12
Manhattan distance is used
in order to build clusters.

13. DATA
MANAGEMENT
DATA
VISUALIZATION
CLUSTER
ANALYSIS
MULTIDIMENSIONAL
SCALING
LABELS
13
DEMS
ECONOMICS
STATISTICS
BUSINESS
STRATEGY
• Finance and
Energy
• Economic policy
• Macroeconomics
• Income
Distribution
• Game Theory
• Health Statistics
• Pure Statistics
• Statistics and
Finance
• Social Issues
• Industrial Economic
• Corporate Finance

14. DATA
MANAGEMENT
DATA
VISUALIZATION
CLUSTER
ANALYSIS
MULTIDIMENSIONAL
SCALING
14
MOVE TO
RSHINY!

15. 1
2
3
SUMMARY
CONCLUSIONS
15
Multidimensional
scaling is a
powerful tool to
visualize data.
We found the main
topics studied by
DEMS researches.
MDS and
Clustering can
show interesting
patterns in data.

16. FUTURE DEVELOPMENTS
Other techniques for scaling, such as
Self Organizing Maps.
Other proximity measures for MDS.
Consider not only singleton into the
bag of words (Association Analysis).
16

17. 17
THANK YOU
FOR YOUR
ATTENTION