2. Interaction matrix related to “transactionality” (see report from Economy team).
3. Interaction matrix related to certain financial attributes (see report from Finances team).
Our goal, as researches of Sciences field, is to build such matrices that fulfills the following
conditions:
Matrices obtained from candidate groups via non-supervised methods, to be an economic
community in terms of the econometric model.
Sparse adjacency matrices with main diagonal entries equal to zero, obtained from the
relationships between certain members of the candidate communities.
Block diagonal matrices whose diagonal elements are square matrices that describes the
interactions of the candidate communities.
The aforementioned matrices will be obtained from two datasets that describes clients in terms
of its transactionality and its financial states1. In particular, these datasets are generated by
the team of Finances following certain criteria (See Finances Team report).
It is important to address that even if the datasets provided by the Finances team was
filtered and certain variables selected to this research, we must be first preprocess it because
of the non structured nature of certain attributes.
On the other hand, we are not going to use yet text mining techniques (filtering/stemming,
stop words) [16], but we are going to implement certain alpha cuts over the results obtained
by non crisp clustering algorithms.
Now, we propose to develop several non-supervised strategies in order to find the required
matrices and for this aim, we will use a set of clustering techniques based on certain principles
both crisp and fuzzy [23, 16], as well as adjacency [21, 12], together with dimensionality
reduction and visualization algorithms used mainly in metagenomic applications [19, 9]. Our
procedure of extracting information from data, in order to construct useful knowledge will be
tested with this problem, but need to be proved later on the Econometric Model esteemed by
Bayesian Inference. Also, for the verification of the information retrieved, it is important the
interdisciplinary work to figure it out if the results are consistent with the possible interactions
within the Bancolombia clients in terms of their value and risk.
This report is organized as follows: section 2 presents generally the methodology to fol-
low, section 3 introduces the feature spaces generated with the data and describe properly
the procedures for its generation, section 4 presents the results and contains a discussion. Fi-
nally, section 5 will address the concluding remarks, and section 6 will provide supplementary
material for this report.
Here some nice pictures: