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Esta
obra está ba=
jo una licencia internacional Creative Commons Atribución-NoComercial
4.0
Sent: 2024/07/12 Accepted:
2024/10/02 Published: 2024/10/15
Abstract This study employs advanced data mining techniques=
to
comprehensively analyze Ecuador's import data spanning
three decades, from 1990 to 2021. By leveraging the k-means clustering algorithm, we meticulous=
ly
identified anomalies within tariff items. The study utilized a comprehensive dataset that encompassed tariff items, years, and critical variables such as import volume,
CIF and FOB values, import cost, and trade attractiveness. The data mining
model generated insightful reports that accurately pinpointed an omalous patterns in tariff items. These
reports empower
experts to delve into the underlying causes of these anomalies, enabling them to
make well-informed decisions
to optimize Ecuador's import strategies. Our research underscores
the transformative potential of data mining in detecting import
anomalies, providing valuable intelligence for the strategic management of
Ecuador's foreign trade. The findings contribute significantly to the prevention of customs fraud
and unfair trade
practices, ultimately enhancing the competitiveness of the
country's import sector.
&n=
bsp;
Keywords: Anomalies, Clustering, Data mining, Foreign trade, K-Means.
=
Resumen Este =
estudio =
emplea <=
span
class=3DSpellE>técnicas =
avanzadas de minería=
de datos pa=
ra realizar un análisis =
exhaustivo de los
datos de importación de Ecuador que abarcan t=
res décadas , de 1990 a 2021. Al aprovechar el algoritmo de agrupamiento k-means, identificamos<=
/span> meticulosament=
e anomalí=
as dentro <=
span
style=3D'letter-spacing:-.75pt'> de las
partidas =
arancelarias . <=
/span>Se utilizó=
un conjunto de datos completo que abarca <=
span
style=3D'letter-spacing:-.75pt'> partidas arancelarias, años y variables clave co=
mo
el volumen de importación , los <=
span
class=3DSpellE>valores CIF y FOB, el costo de importación y el atractivo =
comercial . El modelo de minería de datos generó informes reveladores que identificaron<=
/span> con precisión patrones anómalos =
en las partidas arancelarias . <=
/span>Estos informes permiten a los expertos profundizar
en las causas subyacentes<=
/span>
de estas anomalías<=
/span>,
lo que les permite tomar decisiones bien informadas para optimizar las estrategias =
de importación de Ecuador. Nuestra investigación subraya <=
span
class=3DSpellE>el potencial transformador de la minería <=
/span>de datos para detectar anomalías en las importac=
iones ,
proporcionando inteligenci=
a
valiosa para la gesti&oacu=
te;n
estratégica del com=
ercio
exterior de Ecuador. Los hallazgos contribuyen significativamente a
la prevención del f=
raude
aduanero y las prác=
ticas
comerciales desleales, mejorando en última instancia la competitividad del
sector importador del pa&i=
acute;s .
&=
nbsp;
Palabras clave: =
Anomalías , Clusterizaci&oa=
cute;n , Comercio
Exterior, K-Means, Minería<=
/span> de datos .
=
Introduction Many
countries, such as Ecuador, have meticulously collected
data on their export and import activities for years. These valuable
resources, available on the Central
Bank of Ecuador website, hold enormous
potential for generating productive knowl=
edge. However,
their use has been limited. Regarding imports,=
which
range from raw materials to technology and vehicles to supply nationa=
l industries, decision-making has been based mainly
on empirical approaches and isolated initi=
atives. This aspect has impeded more significant economic
growth. (Quintana
et al., 2021).
&n=
bsp;
Imports play a crucial
role in the Ecuadorian economy,
as the country relies=
heavily
on external products to meet domestic demand and support various
industrial sectors. Analyzing <=
span
style=3D'letter-spacing:-.2pt'>these imports using data mining techniques
allows us to identify patterns, trends, and relationships
invaluable in strategic decision-making.
&=
nbsp;
=
Knowledge
and training are essential in a globalized world where competition intensif=
ies.
Innovative =
strategies and techniques are required to access and strengthen new markets. When applied to imports, data mining provides behavioral patterns that can serve as a basis for defining
solid strategies. However, it is not enough to master economic theor=
y or
replicate successful techniques from other countries. The key is to develop
original strategies deeply rooted in Ecuador's
historical import data.
Morales Zurita (2023)
and Suárez y Amador (2009)
state that information<=
span
style=3D'letter-spacing:-.7pt'> is critical to understanding the past and the present
and, in a certain way, predicting
the future.
&n=
bsp;
=
A data mining process is =
essential for =
transforming a dataset into a complete and relevant one. This set must include detail=
ed
information about the imported products, their origin, quantities, prices,
means of transport, ports of entry, and other relevant aspects.
=
In the information age, where t=
he
amount of available data grows exponentially, data mining has become an indispensable tool for its analysis and strategic use. This discipline ranges
from simple graphical techniques to complex statistical methods,
complemented by artificial
=
intelligence and machine learning algorithms. These allow for solving problems of
automatic grouping, classification, va=
lue
prediction, pattern detection, and attribute association, providing =
invaluable
information for decision-making (López et al., 2023; Suárez y=
Amador, 2009).
&=
nbsp;
The
analysis of imports
with data mining
must require knowledge of statistics,=
machine learning algorithms, and da=
ta
analysis tools (Al Ayub Ahmed et al., 2023). In addition, the quality and reliability of the data used are crucial to obtaining accurate
results and conclusions. Ultimately, data mining offers unprecedented potential to analyze
imports in Ecuador. By applying appropriate techniques and tools,
companies and decision-makers can use this information to optimize their
business strategies, strengthen their import processes, and ultimately boost
the country's economic development.
&n=
bsp;
This
study, which focuses
specifically on Ecuadorian imports, their=
granular
analysis at the tariff =
item
level, detecting anomalies in multiple variables, and using the gravity mod=
el
to support decision-making, provides practical insights directly applicable=
to
your work. Under this context,<=
span
style=3D'letter-spacing:-.5pt'> the document has been structured as follows: Section
2 presents the related works; Section 3 sets out the method=
ology
and methods used; Section 4 details the results obtained; Section 5 presents the discussions; and finally, in the last section, conclusions and future work are offered.
=
Related Works Previous studies on anomaly det=
ection
using data mining are data mining studies have focused more on electronic commerce and the other economic variables
that have affected trade relations between countries. These studies =
have
been based on the gravitational model.
&=
nbsp;
The foreign trade study by
Gonzáles Argote y Ticona Gonzáles (2019) analyzes a landlocked
country's effects on foreign trade. They applied data mining, especially the clustering
technique with the k-means algorithm and Partitioning Around Medoids (PAM), with information
on international trade indicators from 188 countries over ten years.
This aspect was done to detect <=
span
style=3D'letter-spacing:-.5pt'> whether the condition of being landlocked is a limiting factor in the commercial dynamics
of countries. The study considers variables such as per capita incom=
e,
institutional quality, economic integration, latitude, variable indic=
ating Mediterranean identity, econ=
omically active population, land area, a nat=
ural
resources indicator, export cost (USD per container), import cost (USD per container), time to export
(days), time to import (days) (Djayeola =
y Fujs, 2018;
The World Bank Group, 2024).
&n=
bsp;
=
The results showed that in =
recent years, a small subset of =
landlocked countries, =
including Bolivia and Paraguay, have eased the restrictions landlocked countries impos=
e on the costs and
times of export and import. Countries were also identified as having other
types of economic barriers despite having access to the sea. Therefore, they present international trade characteristics
similar to, or even less favorable than, landlocked countries, such as Venezuela. These results are
consistent with the relationships of trade variables proposed by the gravity model of international
trade, where countries were identified that do not have access to the
sea but improved trade relations with other
countries thanks to the improvement in economic terms
such as the Gross
Domestic Product (GDP) of these countries. (Gonzáles Argote y Ticona Gonzáles, 20=
19).
=
Similarly, the study on anomaly
detection has been applied in electronic commerce, as in a survey performed at York University, Canada, where<=
span
style=3D'letter-spacing:-.15pt'> the k-means cluster
segmentation technique is used to investigate anomalies in electronic
commerce transactions. Applyin=
g this
technique generated several clusters, each including records of similar
transactions. Using a mathematical method, they defined a threshold, and if=
the
number of records in that group is greater than the threshold, that group is
called normal; otherwise, it is labeled as an anomaly cluster. Then, they applied
different classification algorithms to investigate each new group, such as
Logistic Regression, Naive Bayes, Nbtree , and R=
adial
Basis Functions (RBF) to evaluate each cluster. With this model,
they identified approximately 2.2% of the transactions analyzed as anomalies (Tan et al., =
2020).
&n=
bsp;
In the study by Wohl and Kennedy (2018), they perfo=
rmed a
Neural Network analysis of International Trade using a data set assembled f=
or a
gravity model of international trade. The objective was to predict trade
relations between countries, so they used the following variables: bilateral
trade, the distance between countries, the GDP of the exporter, and the GDP=
of
the importer; dummy variables, indicating whether the countries share a
language, a border, a colonial
relationship, or a trade agreement; and country or country-year fixed
effects as independent<=
span
style=3D'letter-spacing:-.35pt'> variables. They randomly<=
span
style=3D'letter-spacing:-.35pt'> divided
the data into a training
set and a test set, then
used the data from the training set to create an Ordinary
Least Squares (OLS) linear regression, a Poisson pseudo-m=
aximum likelihood estimator, and a neural network,
and then used the test data to measure how the differe=
nt
methods generalize to new data.
&n=
bsp;
Furthermore, they compared the results with a reference
model, a model with country- fixed effects, and a model with country-year fixed effects. They then compared the neural network
predictions to actual
trade between the United States
and its major
trading partners outside
the sample period to examine whether trade between countries can be
accurately predicted with limited data. Finally, they verified that neural
networks can be efficiently used with a few economic, geographic, and historical variables to generate reasonably accurate predictions about international
trade (Wohl & Kennedy, 2018).
=
Materials and Methods This study presents a detailed
analysis of imports made by Ecuador during the period from 1990 to 2021.
To detect anomalies in import patterns, the Cross-Indu=
stry Standard
Process for Data Mining
(CRISP-DM) methodology<=
span
style=3D'letter-spacing:-.45pt'> has been applied
in conjunction with the Software Process Engineering Me=
tamodel (SPEM)
scheme (Plotnikova et al., 2022).
The data clustering technique has been used with the k-means
algorithm on a data set of Ecuadorian imports obtained
from the website
of the Central Bank of Ecuador (Banco Central del Ecuador, 2023). The data set used in this study contains detailed
information on Ecuadorian imports by tariff heading, year, and country. The variables inc=
luded
in the data set are imported tons, import, cost, insurance and freight (CIF)
value, and accessible on board (FOB) value.
&=
nbsp;
The methodology used in the research This research considers the fol=
lowing
stages: i ) data preprocessing, ii) data mining
technique application, iii) model evaluation, and iv) results evaluation.
Figure 1 shows the stages of the methodology.
=
Figure =
1 SPEM Method=
ology
Data preprocessing The data set used to detect ano=
malies
in Ecuadorian imports contains detailed data by tariff heading, year,=
and country with which Ecuador
has maintained trade
relations from 1990 to 2021. This data set contains
1,471,125 records and 43 variables.
&=
nbsp;
Imported tons and the number of
imports are mainly analyzed. Figure 2 shows the sustained growth
over the years
of imports in the number
of tons, with only one drop recorded in 2016 and another in 202=
0,
then a rebound in 2021.
&=
nbsp;
Figure 2 Tons imported per year
Figure 3 shows an increase in t=
he
number of imported items as the years go by and reflects a drop in the years
2015, 2016, and 2020, reaching
a rebound in 2021, the year with the
highest number of imports.
=
Figure =
3 Number of imported
items per year
In Figure 4, in the countries of the American
continent, we can observe that Ecuador
<=
span
lang=3DEN-US>imports a large number of tons, marking
a significant difference from the rest of the continents.
&n=
bsp;
Figure =
4 Tons per region
Unlike Figure 4, where there is a significant difference in the number of tons imported between the American continent and the rest of the regions, Figure 5 does not show a substantial
difference in the analysis of the number of imported items by region=
.
&n=
bsp;
Figure =
5 Imported tariff items by region
In addition, a statistical anal=
ysis
was performed on each of the variables used in this study to verify their behavior, as seen in Table 1. For this, the variables
were discretized by
frequencies to obtain a more significant number of records in each range.
Without this discretization operation, 99.99% of the data are in the first
range. The leading cause that influences this behavior is abnormal values
(outliers).
&=
nbsp;
Table 1 Statistical values of the variables used
=
MEASURES
VARIABLES
=
IMP_TON
=
IMP_FOB
=
IMP_CIF
=
IMP_FREIGHT_COST
TRADE FORCE=
ATTRACTION
COSTO_X_ =
TONELADA=
=
Count
=
1,471,123
=
1,471,123
=
1,471,123
=
1,471,123
=
1.31E+06
=
1,471,123
=
Mean
=
208.55
=
258,86
=
275.92
=
17.06
=
3.19E+19
=
26,155.89
STD
=
7,706.42
=
5,147.03
=
5,373.74
=
278.90
=
7.79E+19
=
15,425,580
Min
=
0.0000001
=
0.00
=
0.00
=
0.00
=
1.22E+14
=
0.00
25%
=
0.05
=
0.87
=
1.06
=
0.09
=
2.02E+18
=
0.25
50%
=
0.61
=
7.32
=
8.22
=
0.61
=
6.27E+18
=
0.72
75%
=
7.29
=
50.08
=
54.36
=
3.48
=
2.52E+19
=
3.07
Max
=
2,701,835
=
2,300,336
=
2,370,111
=
71,167.97
=
4.96E+20
=
17,964,870,000.00
The original dataset for this s=
tudy
was provided by the Laboratory for Research and Development in Informatics
(LIDI) and obtained from the website of the Central Bank of Ecuador, which
contained null data in the numerical fields, which were attributed using the
Multiple Imputation by Chained Equations (MICE) algorithm (Wulff &
Jeppesen, 2017). In addition, =
the normalization technique call=
ed Z value was implemented on the numerical data to apply =
the
clustering technique.
&n=
bsp;
Apply data mining techniques In this project, the clustering
technique was used to detect anomalies in imports from Ecuador. Clustering is an unsupervised machine-learning task. These algorithms involve
using many unlabeled variables, and this algorithm groups records=
that contain similar
values to each other (Orellana et al., 2022).=
&=
nbsp;
There are several clustering
algorithms; the most well-known and used is k-means, based on centroids. It=
is
a simple and efficient algorithm, so it is necessary first to define how ma=
ny groups (k) the records will be divided
into. The elbow method was applied to determine
the number of clusters, as seen in Figure 6. The ideal values to define the number of groups are 3, 4, and 5.
&n=
bsp;
The variables that have been us=
ed in
this model to identify anomalies in Ecuadorian imports are IMP_YEAR (import
year), IMP_TON (imported tons), IMP_FOB (FOB value of the import), IMP_CIF =
(CIF
value of the import), IMP_FREIGHT_COST (import
cost value), TRADE_FORCE_ATTRACTION (trade attraction force, calculated with the gravity equation),
and COSTO_X_TONELADA. As a parameter, the system requests a tariff i=
tem
level; when running the program,
the model detects
the anomalies of all tariff items per year at the required level. For the data prepara=
tion,
they were normalized with the z-value algorithm and credited with the MICE
algorithm to treat missing data.
=
Figure =
6 Applied elbow method
Once the data was ready, the k-=
means
algorithm, a popular clustering algorithm, was applied. This algorithm groups the data into a specified number of
clusters, in this case, three. The standard deviation value of all records was then calculated for all variables in the clustering algorithm. This step hel=
ps to
identify the variability of the data within each cluster. Then, the average=
of the standard
deviations of each variable was calculated, grouping
them by year and by item=
code.
&n=
bsp;
As
for the averages
of the standard devia=
tions by year and by item,
the values above
and below three standard deviations of the centroids of each cluster
were detected. This approach is based on statistical principles and helps identify
extreme values significantly different from=
the
average, labeling those records as anomalies for k =3D 3.
&n=
bsp;
This procedure was repeated for 4 and 5 clusters,
and anomalies were obtained for k
=3D 3, k =3D 4, and k =3D 5. Finally, it was possi=
ble to
identify the anomalies in Ecuadorian imports, selecting only the records
detected as anomalies
in the three cases. The model generates tables, allowing =
the user to perform a complete analysis of imports at the tariff
item level, year of import, and all variables with outlie=
rs.
&n=
bsp;
Model Evaluatio=
n Validation metrics were used to evaluate
the clustering result.
The goal of clustering is to group similar objects in the =
same
cluster and different objects in different clusters. On the other hand, the
validation metrics are based on the criteria of cohesion, where objects in =
the
same cluster are located as close to each other
as possible; and separation, where
clusters should be wide=
ly
separated from each other, to measure the distances of the nearest clusters=
and
the distance between the most distant clusters or the distance between
centroids.
&n=
bsp;
The Silhouette index is commonl=
y used
to evaluate the quality of the formed clusters, which quantifies how=
well the data has been grouped compared
to how disjoint the clusters
are from each other.
In other words,
the Silhouette index
considers two main aspects of the clusters: cohesion and separation.<=
span
style=3D'letter-spacing:-.05pt'> A high
and positive Silhouette index close to one indicates that the clusters are
cohesive and well separated. A<=
span
style=3D'letter-spacing:-.4pt'> Silhouette index close to zero sugge=
sts
overlap or that points are at or near the boundaries between
clusters, and a negative Silhouette index close to=
-1
indicates that a point might be assigned to the wrong cluster.
=
Table 2 shows the values of the Silhouette index for k equal to 3, 4, and 5. 'k' represents the number of clusters.<=
span
style=3D'letter-spacing:-.65pt'> As can be seen, these are positive =
values
close to 1, indicating that the clusters are cohesive and the groups are we=
ll
separated.
&=
nbsp;
Table 2 Statistical values of the variables used
=
K
SILHOUETTE INDEX
=
3
=
0.987
=
4
=
0.974
=
5
=
0.964
Results Evaluation This
data mining model
generated five comprehensive reports that allowed
for in-depth analysis o=
f the
results to be verified by experts. Each report provides valuable information
for understanding import behavior and detecting potential anomalies.=
&n=
bsp;
The
standard deviation values
report details the year, tariff
item, and standard
deviation values for each variable used in the k-means algorithm.
Additionally, identifying the cluster =
to which each record belongs is included.
The outlier report presents<=
span
style=3D'letter-spacing:1.6pt'> the year, tariff item,
and variables used in the k-means algorithm for each record, indicating whe=
ther
the record is classified as typical or atypical. The label report is a copy=
of
the original dataset but with an additional column displaying the cluster to which each record was assigned. This report allows you to analyze how the k-means
algorithm grouped the imports, which
is based on minimizing
the within-cluster sum of squares and facilitates the identification of
abnormal values in each dataset variable.
&=
nbsp;
The final report presents the y=
ear,
the tariff heading, and an indicator of whether the record was detected as =
an
anomaly by the algorithm with three, four, and five clusters. In addition, a
final column is included, showing the number of matches as an abnormal valu=
e.
Records with the highest number of matches are those that require further
analysis.
&=
nbsp;
=
These generated reports offer a <=
span
lang=3DEN-US style=3D'letter-spacing:-.65pt'> complete and detailed view of the behavior of =
Ecuadorian imports.
Identifying outliers and anomalies allows competent authorities and entitie=
s to
take timely measures, such as conducting further investigations, imposing penalties, or implementing
stricter monitoring to investigate possible customs fraud, unfair tr=
ade
practices, or other irregularities.
=
Results For the analysis
of results, only the information obtained with the model applied
to the
=
level of one of the tariff items was considered, and the following
results were obtained.
&=
nbsp;
Figure 7 graphically displays t=
he
abnormal values of imports in tons of item 1, “Live animals,” over time; those
shown outside the red lines
represent the centroid's third positive=
and negative standard deviation. In=
this
example, imports in tons of live animals in 2002 contain an anomaly,
and indeed, after
reviewing the data set, it was verified
that there was an import
of
240.72 tons to New
Zealand. The anomaly can be verified concerning the average =
tons
of live
animals in 2002,
which is 7.15 tons.
=
Figure =
7 Abnormal <=
/span>values of imports in tons of heading 1 “Live animals,” three clusters
In
Table 3, the item code and the year that was detected
as anomalies for k=3D3, k=3D4, and
k=3D5 are displayed; that is, they have three matches. From this table, ite=
m 87
“Motor vehicles, tractors, bicycles, and other land vehicles, their parts and accessories” was taken as an example to analyze it and find the anomalies in the original
dataset. As can be seen,
this item has anomalies
in the years 1998 and 2021. The analysis was verified in the spreadsheet check tables generated by the model to confirm th=
at
they were indeed anomalies in these years.
&=
nbsp;
Table 3 Tariff items per year were detected as anomalies in the three cases.
TARIFF =
ITEM
YEAR
3 CLUSTER=
S
4 CLUSTER=
S
5 CLUSTERS
COINCIDENCES
=
6
=
2018
=
1
=
1
=
1
=
3
=
9
=
1996
=
1
=
1
=
1
=
3
15
=
1990
=
1
=
1
=
1
=
3
27
=
1991
=
1
=
1
=
1
=
3
32
=
1996
=
1
=
1
=
1
=
3
48
=
1998
=
1
=
1
=
1
=
3
61
=
2007
=
1
=
1
=
1
=
3
61
=
2008
=
1
=
1
=
1
=
3
63
=
2017
=
1
=
1
=
1
=
3
67
=
2007
=
1
=
1
=
1
=
3
79
=
1990
=
1
=
1
=
1
=
3
80
=
2005
=
1
=
1
=
1
=
3
82
=
2016
=
1
=
1
=
1
=
3
87
=
1998
=
1
=
1
=
1
=
3
87
=
2021
=
1
=
1
=
1
=
3
88
=
2008
=
1
=
1
=
1
=
3
90
=
2015
=
1
=
1
=
1
3
There are outliers in item 87 of
Table 4 for the variables tons, FOB, and CIF between 1988 and 2021. For this study,
only the variable
of imported tons was analyzed. In this example, the results generated by t=
he
model with three clusters were verified. However, it should be considered that, in 1998, the clustering algorithm placed=
the anomalous record in cluster
3, and in 2021, it place=
d that
record in cluster 2.
&=
nbsp;
Table 4 Outliers <=
/span>by variable
of item 87 between the years 1998 and 2021
=
YEAR
=
IMP_TON
=
IMP_FOB
=
IMP_CIF
IMP_ =
FREIGHT_=
=
COST
TRADE_ =
FORCE_ =
ATTRACTI=
ON
=
COSTO_X_ TONELADA
=
CLUSTER
=
TARIFF ITEM
=
2021
=
Atypical
=
Atypical
=
Atypical
=
Atypical
=
Normal
=
Normal
Cluster 2
87
=
1998
=
Atypical
=
Atypical
=
Atypical
=
Atypical
=
Normal
=
Normal
Cluster 3
87
When analyzing the Ecuadorian i=
mport
data set, it was found that in 1998, Ecuador imported 8,126.78 tons<=
span
style=3D'letter-spacing:-.75pt'> from Japan, when in that year, the average import
of this item was 48.80 tons. In 2021, Ecuador
imported 29,460.65 tons from China, while the average number of tons imported that year was 114.05 t=
ons.
With these analyzed examples, it was randomly verified that the model for
detecting anomalies in Ecuadorian imports generates correct information.
However, a more systematic verification method can be developed in future w=
ork.
&n=
bsp;
For an import to be labeled as
abnormal with greater certainty, it is when it has been detected as an anomaly
for k =3D 3, k =3D 4, and k =3D 5. Table 5 shows the number of cases detected by the model with one, two=
, and
three matches.
&=
nbsp;
Table 5 Anomalies =
by number
of matches
NUMBER OF MATCHES
NUMBER OF CASES
=
PERCENTAGE
=
1
478
=
83.71
=
2
76
=
13.31
=
3
17
=
2.98
=
Total
571
100
=
Similarly to the =
variables used for this study, it <=
span
lang=3DEN-US style=3D'letter-spacing:-.6pt'> is observed that the =
variable with the most atypical values is the cost per ton, followed by the number of=
tons,
as seen in Table 6.
&n=
bsp;
Table 6 Number of atypical
records per variable
VARIABLE
NORMAL
ATYPICAL
=
COSTO_X_TONELADA
=
9,017.00
=
187.00
=
IMP_TON
=
9,030.00
=
175.00
=
IMP_FREIGHT_COST
=
9,040.00
=
164.00
=
IMP_CIF
=
9,046.00
=
158.00
VARIABLE
NORMAL
ATYPICAL
=
IMP_FOB
=
9,047.00
=
157.00
TRADE_FO=
RCE_ ATTRACTI=
ON
=
=
9,101.00
=
103
=
Total
=
45,180.00
=
840.00
Discussion This study on detecting anomalie=
s in
Ecuadorian imports is particularly relevant to international trade. It
distinguishes itself from previous research in data mining by its specific
approach and detailed scope=
. Unlike
other works that analyze general
economic factors or e-commerce transactions, =
this study
focuses on Ecuadorian imports over 32 years (1990-2021). This allows experts in=
the
field to analyze the factors that influenced these changes, making it a
valuable resource for future negotiations.
Gonzáles Argote and Ticona
Gonzáles (2019)<=
span
style=3D'letter-spacing:-.25pt'> use the data mining clustering technique but with a different purpose: to identify the tra=
de
situation of landlocked countries, including various economic variables. This study only analyzes Ecuadorian imports with varying countries
by tariff heading
and year, detecting
anomalies in variables
such as imported tons, cost per ton, import cost, and CIF and FOB va=
lue.
The
study by Wohl and Kennedy
(2018) uses the gravity model
to detect anomalies and support dec=
ision-making in future trade negotiations. The trade attraction force calculate=
d with this model becomes a relevant indicator
to assess the impact of trade relations
between Ecuador and its
trading partners.
T=
he
results of this study offer valuable information for Ecuadorian authorities
responsible for trade policies. Identifying an=
omalies and understanding import
trends at a granular level
can contribute to i ) investigating potential customs fraud or unfair trade practices, ii) optimizing trade negotiation strategies, iii) making informed
decisions about supplier
diversification and target market selection, and iv)
strengthening the competitiveness of the Ecuadorian import sector.
Conclusions In the results
of the anomaly detectio=
n, it is verified
that the model shows the item and the year in which the v=
alues
of the variables are atypical due to the difference they have concerning the
average of these variables in the same year of importation, fulfilling the =
main objective
of this work.
The present data mining model
applies the clustering technique for =
3, 4, and 5 clusters. At least in one of the cases,
the variables exceed
three standard deviations of their centroids; that=
item
is labeled as an anomaly, and the atypical values are shown in a report. With this process,
the tariff item and the year of importation are indicated precisely. The variables that present the most anomalies in this resea=
rch
are the cost per ton and the number of tons imported.
Regarding the analysis of statistical values,
it is observed that atypical
values exist in all vari=
ables.
For example, analyzing the variable of imported tons, the minimum value is
0.0000001 tons, the maximum is 2,701,835.00 tons,
and the average
is 208.55 tons.
This shows that 99.99%
of the data is in the first
range, and the remaining 0.01%
is distributed in the other five remaining ranges.=
T=
his study demonstrat=
es that the =
data mining clustering technique <=
/span>is efficient <=
/span>for detecting anomalies in a dataset that expe=
rts in
various fields of study can use. As
future work and as a
&=
nbsp;
complement to this data mining model, this process =
can be
further systematized, applying the sam=
e methodology but giving the user the option of searching for anomalies only in the required
items and displaying the results on the screen. This enhancement cou=
ld
significantly reduce processing and search times while providing direct res=
ults
on the screen without based search and the need for more specifics. <=
/p>
Acknowledgments This
work was partially supported by the vice rectorate of Research at Universidad del Azuay for their financial and
academic support and the entire staff in the Computer Science Research &
Development Laboratory (LIDI).
References Al Ay=
ub Ahmed,
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