=Paper=
{{Paper
|id=Vol-2546/paper03
|storemode=property
|title=An empirical comparison of machine learning clustering methods in the study of Internet addiction among students majoring in Computer Sciences
|pdfUrl=https://ceur-ws.org/Vol-2546/paper03.pdf
|volume=Vol-2546
|authors=Oksana Klochko,Vasyl Fedorets
}}
==An empirical comparison of machine learning clustering methods in the study of Internet addiction among students majoring in Computer Sciences==
<pdf width="1500px">https://ceur-ws.org/Vol-2546/paper03.pdf</pdf>
<pre>
58


 An empirical comparison of machine learning clustering
    methods in the study of Internet addiction among
        students majoring in Computer Sciences

                              Oksana Klochko[0000-0002-6505-9455]

                Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University,
                     32, Ostrozhskogo Str., Vinnytsia, 21100, Ukraine
                               klochkoob@gmail.com

                               Vasyl Fedorets[0000-0001-9936-3458]

                     Institute of Higher Education of the NAES of Ukraine,
                            9, Bastionna Str., Kyiv, 01014, Ukraine
                                  bruney333@yahoo.com



       Abstract. One of the relevant current vectors of study in machine learning is the
       analysis of the application peculiarities for methods of solving a specific
       problem. We will study this issue on the example of methods of solving the
       clustering problem. Currently, we have a considerable number of learning
       algorithms which can be used for clustering. However, not all methods can be
       used for solving a specific task. The article describes the technology of empirical
       comparison of methods of clustering problem solving using WEKA free software
       for machine learning. Empirical comparison of data clustering methods was
       based on the results of a survey conducted among students majoring in Computer
       Sciences and dedicated to detecting signs of Internet addiction (IA) as
       behavioural disorder that occurs due to Internet misuse. Empirical comparison of
       Expectation Maximization, Farthest First and K-Means clustering algorithms
       together with the application of the WEKA machine learning system had the
       following results. It described the peculiarities of application of these methods in
       feature clustering. The authors developed data instances’ clustering models to
       detect signs of Internet addiction among students majoring in Computer Sciences.
       The study concludes that these methods may be applicable to development of
       models detecting respondent groups with signs of IA related disorders.

       Keywords: Empirical Comparison, Machine Learning, Clustering, Internet
       addiction (IA), IA detection, Internet disorders, Expectation Maximization,
       Farthest First, K-Means.


1      Introduction

One of research directions in machine learning is the empirical analysis of methods of
solving a specific problem. Let us study this issue on the example of methods of solving

___________________
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
                                                                                      59


the clustering problem. Clustering methods are statistic methods of data analysis that
enable people to group the given selection of data samples into clusters, classes, taxons
depending on the value of their attributes; each of these groups has certain
characteristics. The main idea is to use several clustering methods in order to carry out
an empirical comparison study and determine which methods ensure the most optimal
data grouping while solving a specific problem.
   Machine learning classifies clustering problems as problems for unsupervised
learning. Currently, there is a considerable number of machine learning algorithms that
can be used for clustering, for instance, Expectation Maximization, K-Means,
Hierarchical Clustering etc. But not all of them are suitable for solving a specific
problem. Data clustering algorithms differ by the cluster model type, the algorithm
model type, the nesting hierarchy of clusters, the way of implementation depending on
the data set etc. Because of this, there are also certain requirements to the data set
parameters.
   Popular software products used in machine learning include TensorFlow, WEKA,
MATLAB, MXNet, Torch, PyTorch, Microsoft Azure Machine Learning Studio and
others.
   In this article, we use the WEKA (Waikato Environment for Knowledge Analysis)
free machine learning software [19]. The free WEKA machine learning system gives
direct access to the library of implemented algorithms written in Java.
   Analysis of contemporary studies and publications shows that the issue of analysis
and selection of the machine learning method, which would be optimal for processing
a concrete data set, is popular in the scientific circles. A considerable number of these
studies is dedicated to the application of machine learning methods in the fields of
healthcare and life safety.
   In their article A Performance Comparison of Machine Learning Classification
Approaches for Robust Activity of Daily Living Recognition scientists Rida Ghafoor
Hussain, Mustansar Ali Ghazanfar, Muhammad Awais Azam, Usman Naeem and
Shafiq Ur Rehman studied the application of the machine learning classification
methods to find ways to ensure independent daily living of people who have
Alzheimer’s disease [7]. The idea of the study is to analyze the data registered by
different equipment in order to determine the changes in a person’s behavior that are
relevant for the daily life and social interaction. The paper gives a comparison of the
efficiency levels of five machine learning classification techniques used for the
recognition of a person’s activity (and his/her psychological status). Experimental
findings show that compared to traditional methodologies, these approaches give better
results in determining the activity of the person and his/her psychological and
behavioral peculiarities.
   Jonas Krämer, Jonas Schreyögg and Reinhard Busse studied the speed and efficiency
of medical aid provision using the databases of the Hospital ER [13]. Applying the
Random forest algorithm, the authors developed the model based on the data about the
patient’s provisional diagnosis. The use of the controlled machine learning method and
model training based on the opinion of a specialized doctor allowed them to achieve
high forecasting accuracy (96%) as well as the area under the receiver operating curve
(>0.99).
60


    Abdulhamit Subasi, Jasmin Kevric and M. Abdullah Canbaz developed a hybrid
model of detecting epileptic fits using the Genetic Algorithm (GA) and Particle Swarm
Optimization (PSO) to determine the optimal parameters of application of the Support
Vector Machine (SVM) algorithm [17]. The hybrid algorithm that they suggested can
demonstrate data set classification accuracy of up to 99.38%.
    A considerable number of papers appeared, which are dedicated to diagnosing
Internet addiction (IA) and studying the mechanisms of this disorder among various
social groups. The appearance and use of the Internet has many benefits. However, at
the same time, disorders related to pathological use of the Internet are becoming a social
as well as a psychological problem. Currently, we face an important psychological,
sociocultural and educational issue of detection and prevention of certain pathologies
and steady premorbid conditions (state before the disease) caused by inadequate
Internet use. Cases of IA were first mentioned in 1995 and attracted considerable
attention. Issues related to this one became the research subject of many scientists,
including Lyudmyla Yuryeva and Tatyana Bolbot [10], Marharyta Derhach [4] and
others. Internet Addiction Disorder (IAD) is also called Pathological Internet Use
(PIU). The term “Internet Addiction” was first suggested by Ivan K. Goldberg in 1995.
He describes net addiction as a specific pathology characterized by a wide spectrum of
behavioral and impulse control disorders (lack of control, absence of voluntary
regulation) [1]. In 1996 Goldberg made the first attempt to determine groups of
behavioural and psychological signs and symptoms of IA [18], namely: tolerance;
abstinence syndrome; difficulties in voluntary regulation of Internet-behaviour;
increase of time and financial investments in things related to Internet or computer use;
a shift of a person’s interests towards Internet-related activities; extensive Internet use
that leads to maladjustment. In 1998 Kimberly S. Young defined IAD as an impulsive-
compulsive disorder, which has specific signs or addictions [20; 21]: cyber-sexual
addiction, cyber-relationship addiction, net compulsions, information overload and
computer addiction. IAD is not officially included into ICD-11 for Mortality and
Morbidity Statistics (Version: 04/2019), however, in section 6C51 Gaming disorder the
“Gaming disorder” is described as a “pattern of persistent or recurrent gaming
behaviour (‘digital gaming’ or ‘video-gaming’), which may be online (i.e., over the
Internet)” [8].
    Even though the problem of IA is becoming more and more relevant, there are not
enough scientific papers dedicated to the study of this issue with the help of machine
learning methods. Let us look at some of them. On the basis of the Support Vector
Machine algorithm, including the C-SVM and ν-SVM, and applying the Student’s t-
test to the data set of the survey conducted among 2,397 Chinese students, scientists
Zonglin Di, Xiaoliang Gong, Jingyu Shi, Hosameldin O. A. Ahmed and Asoke K.
Nandic proved the utility of using machine learning methods for detecting and
forecasting the risk of IA [5]. Wen-Huai Hsieh, Dong-Her Shih, Po-Yuan Shih and
Shih-Bin Lin suggested using the EMBAR protected system of web-services based on
the ensemble classification methods and case-based reasoning to study the IA of the
users and prevent the development of this disorder at the initial stages [6]. Hong-Ming
Ji, Liang-Yu Chen and Tzu-Chien Hsiao are currently continuing their research, which
aims to create an IA detector that would work in a real-time mode [9]. The authors
                                                                                           61


suggest studying this issue using an adapted system of continuous real-coded variables
(XCSR), which determines the level of Internet addiction (high-risk and low-risk) on
the basis of the information about the Internet users using the Chen Internet addiction
scale (CIAS) or respiratory instantaneous frequency (IF) [9].
   Thus, based on the above presented statement of the problem as well as taking into
consideration the insufficient amount of research on the application of machine learning
methods to IA diagnosing, we determine the aim of our research, which is to conduct
an empirical comparison of clustering methods within the WEKA machine learning
system in the course of studying the IA disorder among students majoring in Computer
Sciences.


2       Selection of methods and diagnostics

Data regarding the spread and severity of IA among students majoring in Computer
Sciences were received from an online survey, which used a questionnaire drafted with
the help of Google Forms. 263 students majoring in Computer Sciences and coming
from different oblasts of Ukraine participated in the experimental study. The data set is
presented in the ARFF format and consists of 8 attributes (Fig. 1). The data set contains
the fields described in Table 1.

    @relation answer_IA

    @attribute age numeric
    @attribute sex {female,male}
    @attribute 3 {no,undefined,yes}
    @attribute 4 {no,undefined,yes}
    @attribute 5 {no,undefined,yes}
    @attribute 6 {no,undefined,yes}
    @attribute 7 {no,undefined,yes}
    @attribute 8 {no,undefined,yes}

    @data
    18,male,yes,no,no,no,no,yes
    28,male,undefined,no,no,no,no,yes
    20,female,yes,yes,yes,no,no,no
    22,male,yes,no,no,no,no,no
    …
Fig. 1. Data set on the state of IA among students majoring in Computer Sciences, presented in
                                        the ARFF format

Cluster analysis is one of the tasks of database mining. Cluster analysis is a set of
methods of multidimensional observations or objects classification, based on defining
the concept of distance between the objects and their subsequent grouping (into
clusters, taxons, classes). The selection of a concrete cluster analysis method depends
62


on the purpose of classification [12]. At the same time, one does not need a priori
information about the population distribution. This approach is based on the following
presuppositions: objects that have a certain number of similar (different) features group
in one segment (cluster). The level of similarity (difference) between the objects that
belong to one segment (cluster) must be higher than the level of their similarity with
the objects that belong to other segments [12].

     Table 1. Data structure on the state of IA among students majoring in Computer Sciences.

Attributes Сontents/Questions                        Type        Statistics
age        Age of the student                        Numeric     Minimum 16
                                                                 Maximum 59
                                                                 Mean 19.756
                                                                 StdDev 6.806
sex            Student’s sex                         Nominal     Female 199
                                                                 Male 63
3              Can’t imagine my life without the Nominal         yes 184
               Internet                                          undefined 39
                                                                 no 39
4              When I cannot use the Internet I fell Nominal     yes 81
               anxiety, irritation                               undefined 134
                                                                 no 47
5              I like “surfing” the Net without a Nominal        yes 121
               clearly defined purpose                           undefined 112
                                                                 no 29
6              I can abstain from food, sleep, going Nominal     yes 248
               to classes, if a have a chance to use             undefined 7
               the Internet for free                             no 7
7              I prefer meeting new people over the Nominal      yes 185
               Internet rather than in real life                 undefined 37
                                                                 no 40
8              I often feel that I’ve spent not enough Nominal   yes 178
               time playing computer games over                  undefined 61
               the Internet, I constantly wish to play           no 23
               longer

     Let us look at one of cluster analysis algorithms [12].
     Output matrix:
                                         x       ⋯ x
                                  X=         ⋮   ⋱   ⋮      .
                                         x       ⋯ x
Let us move to the matrix of standardized Z values with elements:

                                         z =         ;

where j = 1, 2, …, n – index number, і = 1, 2, ... , m – observation number;
                                                                                       63


                                    x =    ∑      x ;


                    s =       ∑    (x − x ) =            x          − (x ) .

There are several ways to define the distance between two observations zi and zv:

1. weighted Euclidean distance, which is determined by the formula

                        ρ (z , z ) =      ∑      w (z − z ) ;

where wl is the “weight” of index; 0<wl≤1; if wl=1 for all l = 1, 2, …, n, then we get the
usual Euclidean distance
                              ρ (z , z ) = ∑ (z − z ) ;

2. Hamming distance:
                             ρBH (zi ,zv ) = ∑       |zi -zv |;

in most cases this way of distance measuring gives the same result as the usual
Euclidean distance, but in this case the influence of non-systemic large differences
(runouts) decreases;

3. Chebyshev distance:
                            ρBCH (zi ,zv ) = max |zi -zv |;

it is best to apply this distance in order to determine the differences existing between
the two objects using only one dimension;

4. Mahalanobis distance:

                      ρBМ (zi ,zv ) =   ( −      )         ( −          ),

where S is covariance matrix; this distance measurement gives good results when
applied to a concrete data group, but it does not work very well, if the covariance matrix
is calculated for the whole data set;
5. Distance between peaks:
                                                      |zi -zv |
                             ρBL (zi ,zv ) = ∑                  ;
                                                       zi +zv

presupposes independence of random variables, which indicates the distance in the
orthogonal space.
   It is best to choose from the above described distance measures after the
consideration of the structure and characteristics of the data sample.
   Let us present the received measurements in the form of distance matrix:
64


                            0         ρ      ρ     … ρ
                           ρ            0    ρ     … ρ
                         ⎛ …           …      …    … … ⎞.
                       R=⎜
                           ρ          ρ      ρ     … ρ ⎟
                            …          …      …    … …
                         ⎝ρ           ρ      ρ     … 0 ⎠
As the R matrix is symmetric, i.e. ρiv=ρvi, we may confine ourselves to off-diagonal
matrix elements.
   Using the distance matrix, we can implement the agglomerative hierarchic procedure
of cluster analysis. Distances between clusters are determined as the closest or the
farthest ones. In the first case, the distance between the clusters is the one between the
closest elements of these clusters, in the second case, it is the one between the two
farthermost located. The principle of the work of agglomerative hierarchic procedures
lies in a consequent grouping of elements, starting from the ones closest to each other
and those that are farther and farther apart. During the first step of the algorithm, every
observation zі (i = 1, 2, ..., m) is viewed as a separate cluster. Then, during every next
step of the work of the algorithm, two closest located clusters are grouped together and
then once again the distance matrix is built, but its dimension decreases by one. The
algorithm stops its work when all the observations are grouped into clusters.
   Let us look at the algorithms we used while clustering the data set regarding the state
of IA disorder among students majoring in Computer Sciences:

1. EM (Expectation Maximization)
Determines the probability distribution for every object, which indicated its
belongingness to each cluster. EM methods [11]: Maximum Likelihood Estimation
(MLE) or Maximum a Posteriori (MAP). Description of the algorithm is shown in
Fig. 2 [11]: at the E-stage (expectation) we calculate the estimated likelihood; at the M-
stage (Maximization) we calculate the maximum likelihood estimation, increasing the
expected likelihood, calculated at the E-stage; its value is used for the E-stage at the
next iteration. The algorithm is repeated until its convergence.




          Fig. 2. Description of how the algorithm ЕМ works from 10,000 feet [11].

2. K-Means algorithm
                                                                                                  65


Aims to partition n observations into k clusters in such a way that each observation
belongs to the cluster with the nearest mean value. The shortest distance between the
observations and the nearest mean value may be calculated by minimizing the sum of
squares of the distances [14] (Fig. 3).




                                Fig. 3. K-Means algorithm [14].

3. Farthest First algorithm
This is a modification of a K-Means algorithm, in which the initial selection of centroids
is 2 and higher. Centroids are determined following the remoteness principle, i.e. the
point farthest from the rest is selected first. The Farthest First algorithm is described in
Fig. 4 [3].




Fig. 4. Farthest-first traversal of a data set [3]. Take the distance from a point x to a set S to be
                                    d(x, S) = miny∈S d(x, y) [3].


3      Results and Discussion

To cluster data using the WEKA platform, we will use Weka.clusterers.EM,
Weka.clusterers.SimpleKMeans and Weka.clusterers.FarthestFirst algorithms [19].
   We check the application of clustering algorithms that can be assigned to two classes
of clustering algorithms, i.e. distribution based (Expectation Maximization) and
centroid-based (K-Means, Farthest First). Such selection is motivated by the fact these
66


algorithms have long been used to cluster different types of data in many fields and are
considered to be effective.
   Dunn, DB, SD, СDbw and S_Dbw were selected as validity indices for testing [2;
15; 16] (Table 2). In the CDbw index the distance from the point to multitude set in the
course of selecting cluster element can be calculated in different ways. In this study,
we use the sum of distances of already existing “representatives” of the cluster to each
cluster element to calculate this distance. The element, on which the maximum was
reached, was selected as the next “representative” of the cluster.

       Table 2. Optimal number of clusters, calculated with the help of quality indices.

                                                 Algorithms
     Index
                             Expectation Maximization k-Means              Farthest First
     Dunn                    3                          6                  6
     DB                      3                          6                  4
     SD                      3                          3                  3
     СDbw                    3                          3                  3
     S_Dbw                   3                          5                  4

   If the data set has no cluster structure, then such situation is not determined with the
help of validity metrics. While using K-Means and Farthest First (Table 2) the numbers
of clusters for the two algorithms that were selected as optimal by the majority of
indices, can only nominally be defined as cluster structure. As the work of Expectation
Maximization algorithm is based on determining the probability of evaluating
maximum similarity, the indices calculated for this algorithm are more homogenous.
The structure, which is characterized by a small number of clusters that also have to be
compact and separable, is determined to be the best one. Judging by the results of
evaluation of clustering using the validity indices, we may consider that k-Means and
Farthest First algorithms are most likely to give worse clustering results than the
Expectation Maximization algorithms.
   To cluster the data, we select training/testing using the percentage split option. As a
data set for training (model building) we select 66% of data from the set. As a data set
for testing we select 34% of data from the set. In addition, we select number of clusters
“3” in algorithm settings.
   We received the following results:
1. In the course of application of the EM clustering algorithm, according to the built
   clustering model based on the training data set, three clusters were determined, their
   characteristics are given in Table 3.
Cluster 0 (63% of respondents): The average age of respondents in this cluster is 17.
The group consists predominantly of women. The characteristic feature of the
representatives of this group is that they are unable to imagine their life without the
Internet. There are variations in the levels of anxiety and irritation, if there is no
possibility to use the Internet. There are also varying opinions regarding the aimless
use of the Internet. As for other attributes, disorders related to IA may be observed in
                                                                                      67


the insignificant number of respondents, who belong to this cluster. The behavioural
model of the representatives of this cluster demonstrated Internet centration in the
psychic reality of a personality, which is accordingly reflected in their activity and
behavior, other life interests as well as significance of everyday activities lose their
importance. The stated tendencies are linked to IA.

               Table 3. Model and evaluation on test split by EM algorithm.

                                                        Clusters
                                      0             1              2
         Attributes Indications
                                      (0,63)        (0,13)         (0,24)
                                      112.1491      24.7781        44.0727
         age         mean             17.4469       36.2459        19.2906
                     std. dev.        1.5994        10.0785        2.243
         sex         female           108.8714      16.0638        5.0648
                     male             2.2778        7.7143         38.0079
         3           no               22.7034       3.1864         10.1102
                     undefined        16.0405       6.4026         4.5569
                     yes              73.4052       15.1891        29.4057
         4           no               54.392        13.8263        27.7817
                     undefined        23.6012       5.1903         7.2085
                     yes              34.156        5.7615         9.0825
         5           no               45.3302       19.3167        26.3531
                     undefined        15.1791       2.1415         5.6794
                     yes              51.6398       3.32           12.0403
         6           no               106.1573      22.7561        41.0866
                     undefined        1.0117        1.0098         1.9785
                     yes              4.9802        1.0122         1.0076
         7           no               81.1224       20.5492        27.3284
                     undefined        11.5501       2.168          11.282
                     yes              19.4767       2.061          5.4624
         8           no               89.4444       19.3333        9.2223
                     undefined        7.2533        1.1937         9.553
                     yes              15.4514       4.2512         25.2975

   Cluster 1 (13% of respondents): For the representatives of this group the average
value of the age attribute is 36 and it varies greatly. This is the oldest age group if
compared with other clusters. This group has the largest share of women.
Representatives of this group, predominantly, cannot imagine their life without the
Internet. Thus, according to the centroid values of the attributes, we may diagnose IA
related Internet centration in the psychic reality of a personality, which is accordingly
reflected in their activity and behavior; other life interests as well as significance of
everyday activities lose their importance. There are predominantly no other signs of IA
related disorders.
   Cluster 2 (24% of respondents): The probabilistic average of the age attribute among
the representatives of this group is middle-aged in comparison with other groups and is
19. Male representatives significantly dominate in this group. Regarding the inability
68


to imagine their life without the Internet, opinions differed, however, predominantly
respondents believe they have this addiction. Judging by the values of attributes 4, 5, 6
and 7, the vast majority of this group’s representatives declare that they do not have
other signs of IA. However, the feeling of the lack of time spent playing computer
games over the Internet, which was confirmed by the vast majority of respondents, is a
warning signal that may signify the existence of IA related disorders. Thus, the
characteristic feature of this group is that most of its representatives have IA related
disorders such as: Internet centration in the psychic reality of a personality; behavioral
impulse control disorders related to online gaming. These people are in the risk group
for developing IA related disorders.
2. In the course of application of the Farthest First algorithm, according to the built
   clustering model based on the training data set, there have also been three clusters
   formed; their characteristics are given in Table 4.

           Table 4. Model and evaluation on test split by Farthest First algorithm.

                                                 Clusters
                  Attributes
                               0             1               2
                  age          16.0          22.0            20.0
                  sex          female        male            male
                  3            yes           undefined       yes
                  4            undefined     no              yes
                  5            no            yes             undefined
                  6            no            no              undefined
                  7            no            undefined       undefined
                  8            no            undefined       no

    Cluster 0: Contains data instances of the youngest age group, whose age centroid
attribute is 16. According to the value of the sex centroid attribute, the group is made
up of mostly female data instances. The representatives of this group cannot imagine
their life without the Internet, i.e. there is obvious Internet centration in the psychic
reality of a personality. Respondents cannot clearly determine whether they feel either
anxiety or irritation if they do not have the possibility to use the Internet. Judging by
other attributes, data instances of this cluster do not have IA related disorders.
    Cluster 1: This cluster contains data instances of an older age group, the age attribute
centroid of which is 22. The value of the sex attribute centroid in this cluster is male. A
characteristic feature of the cluster is undecidedness regarding the vital need to use the
Internet, prevalence of Internet relations over actual real interactions, feeling the lack
of time spent playing computer games over the Internet (attributes 3, 7, 8 equal
undefined). The value of the yes centroid of attribute 5 shows inclination to use the
Internet without a concrete purpose. To give an overall characteristic, this group has
signs of IA, i.e. behavior control disorders related to Internet use.
    Cluster 2: By the value of the age attribute centroid, 20, this cluster contains data
instances of the middle age group if compared with other clusters. The sex attribute
centroid in this cluster is male. The representatives of this cluster cannot imagine their
life without the Internet and feel anxiety and irritation when they do not have the
                                                                                          69


possibility to use the Internet. They are characterized by their undecidedness regarding
the vital need to use the Internet; giving up other life interests and everyday activities
for the sake of free Internet use; prevalence of online relations of real-life interactions
(value of attributes 5, 6, 7 is undefined). Thus, the representatives of this cluster have
signs of IA, the priority significance of the Internet and behavior control disorders,
related to Internet use. Compared to other groups, they are in the risk group for
developing IA related disorders.
3. In the course of application of the K-Means algorithm to the clustering model built
   on the basis of the training data set three clusters have also been formed, their
   characteristics are presented in Table 5.

             Table 5. Model and evaluation on test split by K-Means algorithm.

                                                 Cluster
                  Attribute
                               0             1              2
                  age          18.4194       21.8605        20.9552
                  sex          female        male           female
                  3            undefined     yes            yes
                  4            no            no             no
                  5            no            no             no
                  6            no            no             no
                  7            no            no             no
                  8            no            yes            no

   Cluster 0: Contains data instances of the youngest age group, whose age attribute
centroid is about 18. According to the sex attribute centroid, mostly female data
instances are present in the groups. The representatives of this group cannot clearly
determine whether they have a vital need to use the Internet. As for other indices,
respondents state absence of signs of IA related disorders.
   Cluster 1: This cluster contains data instances of the older age group, whose age
attribute centroid is about 22. The value of the sex attribute centroid in this cluster is
male. Characteristic features of data instances that belong to this cluster include the
vital need to use the Internet, feeling the lack of time spent playing online computer
games as well as the systemic need to play longer. The overall characteristic of this
cluster is the presence of signs of IA, i.e. behavior control issues related to Internet use,
namely, gaming Internet addiction. If compared with other cluster, they belong to the
risk group that may develop IA related disorders.
   Cluster 2: By the value of age attribute centroid, which is about 21 years, compared
to other clusters, this cluster contains data instances of medium age group. The sex
attribute centroid is female. The representatives of this cluster cannot imagine their life
without the Internet. Judging by centroids of other characteristics, respondents of this
cluster do not have Internet-related disorders. Thus, the representatives of this cluster
have only IA signs associated with the utmost significance of the Internet.
   The cluster distribution of test data in the course of application of the three
algorithms – the Expectation Maximization, Farthest First and K-Means – using the
built training models is presented in Table 6. Thus, as it can be seen from the table, the
70


algorithms have determined three data groups. Clusters were formed, which included
71:12:7, 67:4:19 and 33:15:42 data instances respectively. There is a cluster that has
the largest number of data instances; a group, which has the least data instances
(exceptions); a group that includes several times more data instances than the smallest
group.

     Table 6. Clustered Instances determined using Expectation Maximization, K-Means and
                                    Farthest First algorithms.

Clusters     Expectation Maximization        Farthest First algorithm     K-Means
             Instances   %                   Instances    %               Instances      %
0            67          74                  71           79              33             37
1            4           4                   12           13              15             17
2            19          21                  7            8               42             47

    Fig. 5, Fig. 6 and Fig. 7 present a graphic representation of clusters by age
characteristic of data instances, which are built using the training data set and received
in the course of implementation of the Expectation Maximization, the Farthest First and
the K-Means algorithm respectively. As we can see, the formed clusters differ from
each other by the age attribute. For instance, Cluster 0, which contains most data
instances, contains instances of respondents of a younger age, if formed through the
application of the Expectation Maximization algorithm (Fig. 5). On the other hand, the
same cluster received through the implementation of the Farthest First algorithm,
contains data instance of various age groups (Fig. 6). Also, a small number of data
instances of various age groups is present in Cluster 2, received in the course of
implementation of the K-Means algorithm (Fig. 7). Cluster 0 and Cluster 2 formed with
the Expectation Maximization algorithm as well as Cluster 1 and Cluster 2 formed with
the Farthest First algorithm contain homogeneous age groups, and Cluster 0 та Cluster
1, formed with K-Means algorithm.




Fig. 5. Plot of cluster distribution applying the Expectation Maximization algorithm depending
                                      on the age group attribute
                                                                                               71




 Fig. 6. Plot of cluster distribution applying the Farthest First algorithm depending on the age
                                          group attribute




Fig. 7. Plot of cluster distribution applying the K-Means algorithm depending on the age group
                                              attribute

   Fig. 8, Fig. 9 and Fig. 10 present a graphic representation by sex attribute of clusters
formed through the application of the Expectation Maximization, Farthest First and K-
Means algorithm respectively. The analysis of Fig. 8, which visualizes clustering
through application of the Expectation Maximization algorithm, shows that Cluster 0
contains only female data instances. Clusters 1 and 2 have date instances of both sexes.
Female data instances prevail in Cluster 1 and male ones in Cluster 2. Unlike Clusters
formed by the Expectation Maximization algorithm, all the clusters formed by the
Farthest First algorithm contain data instances of both sex groups (Fig. 9). Female data
instances significantly prevail in Cluster 0. All the clusters built using the K-Means
algorithm, contain both male and female data instances (Fig. 10).
72




Fig. 8. Plot of cluster distribution applying the Expectation Maximization algorithm depending
                                         on the sex attribute




    Fig. 9. Plot of cluster distribution applying the Farthest First algorithm depending on the sex
                                                attribute


4         Conclusion

In the course of empirical comparison of Expectation Maximization, Farthest First and
K-Means algorithms using the WEKA machine learning system to study the signs of
IA related disorders among the students majoring in Computer Sciences, the following
conclusions have been made:
1. As a result of empirical comparison of Expectation Maximization, Farthest First and
   K-Means algorithms using the WEKA machine learning system, we developed
                                                                                              73


   models of data instances’ clustering to determine the signs of internet addiction
   disorders among students majoring in Computer Sciences.
2. The implementation of the Expectation Maximization, the K-Means and the Farthest
   First algorithms each resulted in the formation of 3 clusters. The results of clustering
   demonstrate that Internet centration in the psychic reality of a personality is a
   characteristic feature of the respondents that took part in the survey. This also reflects
   accordingly in their activity and behavior, diminishing other life interests and the
   significance of everyday activities. In addition, in the course of implementation of
   the Expectation Maximization algorithm, a cluster was formed, whose
   representatives have behavior control disorders, related to online gaming. These
   respondents are in the risk group for developing IA related disorders.
3. Expectation Maximization, Farthest First and K-Means algorithms of data clustering
   differ by their algorithm model, however, from the point of characteristic features,
   they produce relatively similar clusters, thus implementing optimized clustering. At
   the same time, when a data set was grouped into three clusters by implementing these
   algorithms, the clusters differed by cluster model, namely, by the number of data
   instances in each cluster, their structure and value of attribute centroids.
4. Judging by the evaluation results of clustering validity using the validity indices, we
   can state that most likely the K-Means and Farthest First algorithms show worse
   clustering results than the Expectation Maximization algorithm.
5. Intellectual analysis of the data set regarding the situation with IA among students
   majoring in Computer Sciences with the application of clustering methods has
   shown that the methods studied above may be considered suitable for developing
   models for detecting respondent groups with the signs of IA related disorders.




  Fig. 10. Plot of cluster distribution applying the K-Means algorithm depending on the sex
                                             attribute
74


Our conclusions may help to determine the signs of IA related disorders among students
majoring in Computer Sciences, forecasting the risk of IA and development of services
aimed at IA prevention.


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