=Paper=
{{Paper
|id=Vol-3026/paper7
|storemode=property
|title=Applying Clustering Technique and Association Rule to Analyze Laptop Usage Behavior of Students
|pdfUrl=https://ceur-ws.org/Vol-3026/paper7.pdf
|volume=Vol-3026
|authors=Nguyen Van Chuc,Nguyen Ha Nhu Ngoc
}}
==Applying Clustering Technique and Association Rule to Analyze Laptop Usage Behavior of Students==
https://ceur-ws.org/Vol-3026/paper7.pdf
Applying Clustering Technique and Association Rule to
Analyze Laptop Usage Behavior of Students*
Nguyen Van Chuc** [0000-0003-2618-5659] and Nguyen Ha Nhu Ngoc
University of Economics, The University of Danang, Vietnam
chuc.nv@due.edu.vn, ngocnhn43k08@due.udn.vn
Abstract. A laptop is a typical technological product with high mobility qualities
that allows everyone to learn and work from anywhere. These days, laptops are
in high demand, particularly among students. There are numerous competing
brands on the market with full lines, varieties, configurations, and prices ranging
from inexpensive to high-end, making it difficult for customers to buy. Analyzing
the behavior of students using laptops to discover trends and factors influencing
their decision to buy a laptop and thus assisting them in making the best choice
when choosing. It is also extremely beneficial for laptop distributors and mer-
chants because it helps them to reach out to a larger number of potential custom-
ers. The article focuses on applying clustering techniques and association rules
in data mining to analyze the laptop usage behavior of students. Some solutions
are provided based on the acquired results to assist organizations in understand-
ing customer characteristics and making better business decisions.
Keywords: Data Mining, Behavior Analysis, Clustering, Association Rule.
1 Introduction
The Information Age is rapidly and strongly evolving, resulting in the birth of a slew
of extremely modern and intelligent electronic devices. It is impossible to discuss smart
devices without mentioning laptops. Recently, as a result of the global pandemic of
Covid-19, there has been an increase in the use of mobile devices such as laptops for
communication, distance learning, and knowledge learning based on Google’s plat-
form. There are currently too many laptop lines on the market, making it difficult for
users to choose the brand, function, and price that are reasonable and best suited to their
personal use requirements. It’s also a common question among students. Faced with
this reality, distributors and retailers must understand customer psychology, needs, and
preferences in order to develop effective business policies, advertising, and marketing
* Copyright © by the paper’s authors. Use permitted under Creative Commons License Attrib-
ution 4.0 International (CC BY 4.0). In: N. D. Vo, O.-J. Lee, K.-H. N. Bui, H. G. Lim, H.-J.
Jeon, P.-M. Nguyen, B. Q. Tuyen, J.-T. Kim, J. J. Jung, T. A. Vo (eds.): Proceedings of the
2nd International Conference on Human-centered Artificial Intelligence (Computing4Hu-
man 2021), Da Nang, Viet Nam, 28-October-2021, published at http://ceur-ws.org
** Corresponding author.
68 Chuc and Ngoc
strategies to increase market share and attract customers, particularly students-a poten-
tial customer source for this item.
2 An overview of data clustering techniques and association
rules
2.1 An introduction to clustering techniques
Data clustering is the process of grouping given objects into clusters so that objects in
the same cluster are as similar as possible and objects in different clusters are as differ-
ent as possible. The goal of clustering is to determine the inner groupings of data. There
are numerous clustering techniques available, including partition clustering, hierar-
chical clustering, density-based clustering, and so on [1].
2.2 An introduction to association rules
The goal of Association Rules (AR) in data mining is to find relationships between
objects in large amounts of data. The fundamental of the AR is summarized [2]. Given
the transaction database T contains the set of transactions t1, t2…, tn.
T = {t1, t2…, tn}. Each transaction (ti) is made up of a set of objects I (itemset).
I = {i1, i2…, im}. A k-itemset is an itemset made up of k items.
The purpose of AR is to discover associations (correlations) between items. These as-
sociation rules take the form of X →Y, 𝑋 ∩ 𝑌 = ∅ (1)
Where X: antecedents; Y: consequents
Support and confidence are two crucial criteria in evaluating association rules. The for-
mula for calculating the support and confidence of the association rule X → Y [2, 3]:
𝑠𝑢𝑝𝑝𝑜𝑟𝑡(𝑋 → 𝑌) = 𝑃(𝑋 ∪ 𝑌) = 𝑛 (𝑋 ∪ 𝑌)⁄𝑁 (2)
𝑐𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒(𝑋 → 𝑌) = 𝑃(𝑌|𝑋 ) = 𝑛 (𝑋 ∪ 𝑌)⁄𝑛(𝑋) (3)
Where n(X): Number of transactions containing X; N: Total number of transactions.
AR with support and confidence greater than or equal to the minimum support (min
sup) and minimum confidence (min conf) are referred to as strong rules [2, 3].
3 Using clustering technique and association rule to analyze
student laptop usage behavior
3.1 Problem description
Clustering techniques and association rules are used to analyze the laptop usage behav-
ior of students.
Input: information about students, laptops and factors influencing purchase.
Output: provide the characteristics, behavior of using laptops across majors, predict
usability, and the relationship between factors when students decide to buy a laptop.
Applying Clustering Technique and Association Rule to Analyze Laptop Usage Behavior of
Students 69
3.2 Implementation scenario for student laptop usage behavior analysis
system
Fig. 1. Implementing a student laptop behavior analysis system
Step 1: Data collection and preprocessing. From March to May 2021, 1100 samples
were collected through an online questionnaire survey of students from University of
Economics - The University of Danang. After data preprocessing, Table 1 shows the
data structure.
Step 2: Deploying cluster and association rule models. The model was created using
Microsoft Business Intelligence Development Studio (BIDS) data mining tool. The dis-
covered knowledge is very intuitive, easy to understand, and simple to apply [4, 5].
Step 3: The knowledge discovered from the Cluster and Association rule models used
to analyze laptop usage behaviour of students. (Fig.1)
Table 1. Data description
No. Attribute name Data types Value domain Meaning
1 ID Interval 1 - 1100 Individual survey order
2 GioiTinh Nominal Male, Female Gender of the students
3 QueQuan Nominal Da Nang, Hue… The student’s hometown
4 NamHoc Nominal 1st, 2nd year Year students are studying
5 Khoa Nominal Banking… Faculty
6 Nganh Nominal Accounting… Major
7 NgheNghiepGD Nominal Farmers… Parents’ occupations
8 ChiTieuHangThang Nominal 1.5 million VND Monthly budget for a student
9 ThuongHieu Nominal Asus, Dell… Brand of laptop
10 ThoiGianMua Nominal Under 6 months When purchasing a laptop
11 Gia Nominal 15 million VND Laptop pricing
12 MucDichSuDung Nominal Studying, … Purpose of buying a laptop
13 MucDoHaiLong Interval 1→5 Level of satisfaction
14 ThongTinMua Nominal Websites… Sources of information
15 NoiMua Nominal FPT Shop ... Laptop stores
16 YeuToThuongHieu Nominal Very important... Factor of Brand
70 Chuc and Ngoc
17 YeuToCauHinh Nominal Important… Factor of Configuration
18 YeuToTocDoXuLy Nominal Normal... Factor of Processing Speed
19 YeuToGiaCa Nominal Unimportant... Factor of Price
20 YeuToKieuDang Nominal Normal… Factor of Style
21 YeuToUyTin Nominal Very important... Factor of Retailer Reputation
22 YeuToBaoMat Nominal Very important... Factor of Confidential Mode
23 YeuToBaoHanh Nominal Very important... Factor of Warranty
24 YeuToKhuyenMai Nominal Very important... Factor of Promotion
Fig. 2. The results of data clustering
According to the model’s results (Fig.2), Table 2 shows six clusters:
Table 2. Cluster characteristics
Cluster Size Cluster characteristics
Female in 3rd year, parents are - Configuration, Processing speed:
farmers, the cost between 10 Normal; Brand, Warranty, Promo-
259
Cluster 1 and 15 million VND. Seeking tion, Price, Retailer reputation,
(23.5%)
information from relatives and Style: Important; Security: Unim-
friends, expert → Dell, Asus. portant.
Female in 4th year, parents are - Processing speed, Style: Normal;
farmers, and the cost between Brand, Configuration, Warranty, Se-
210
Cluster 2 10 and 15 million, according to curity, Promotion, Retailer reputa-
(19.1%)
information obtained from rel- tion: Important; Price: Very im-
atives and friends → Dell. portant.
Female in 2nd, 3rd years, par-
ents are government employ- - Promotion: Normal; Price, Config-
196 ees, self-employed. It cost over uration, Processing speed, Warranty,
Cluster 3
(17.8%) 25 million VND to seek infor- Security: Important; Brand, Retailer
mation from family and sales- reputation, Style: Very important.
people → Apple.
151 Male in 2nd and 3rd years, par- - Brand, Warranty, Security, Promo-
Cluster 4
(13.7%) ents are farmers, and the cost tion, Retailer reputation: Normal;
Applying Clustering Technique and Association Rule to Analyze Laptop Usage Behavior of
Students 71
from 10 to 15 million VND, Price, Configuration, Processing
seeking purchasing information speed: Important; Style: Unim-
salespeople → Dell and Asus. portant.
Male in 3rd year, parents are
farmers and self-employed. - Security: Normal; Promotion,
The cost between 15 and 20 Brand, Warranty, Price, Retailer rep-
132
Cluster 5 million VND. They seek pur- utation: Important; Style: Unim-
(12.0%)
chasing information from portant; Configuration, Processing
salespeople and expert guid- speed: Very important.
ance → Dell and HP.
Female in 2nd year, parents are
- Processing speed: Normal; Brand,
farmers with laptop prices un-
Warranty, Price, Retailer reputation:
152 der 10 million VND, referring
Cluster 6 Important; Security, Style, Configu-
(13.8%) to buying information from
ration: Unimportant; Promotion:
family, friends, and salespeo-
Very important.
ple → Asus and Acer.
Fig. 3. The results of association rule model.
Here are some association rules (Fig.3):
R1: With a price of 25 million VND, second-year Foreign Trade students in Da Nang
are interested in brand design and security issues when purchasing a laptop that primar-
ily uses the Apple brand, with 100 percent confidence.
R2: Students of Commerce and Management Information Systems (MIS) from
Quang Nam, Ha Tinh, are interested in warranty, configuration, and brand factors when
purchasing a laptop with a price range of 10 to 15 million VND. Dell is the most com-
monly used brand, with 75 percent confidence.
R3: With a price range of 10 to 15 million VND, third-year Accounting students in
Dak Lak, who are concerned with price, promotion, and warranty when purchasing a
laptop, primarily use the Asus brand, with 63 percent confidence.
R4: With a price of less than 10 million VND, Accounting and Commerce students
in Quang Nam and Hue, are interested in the price, promotion, and warranty factors
when purchasing laptops that primarily use the Asus and Acer brands, with 60 percent
confidence.
72 Chuc and Ngoc
R5: With a price range of 15 to 20 million VND, a male student majoring in MIS
whose parents are freelancer, is interested in configuration factors, processing speed,
and warranty when purchasing HP laptops, with 53 percent confidence.
4 Proposing data-driven marketing and CRM solutions
4.1 Describing product feature
The clustering results show that Dell and Asus are two brands that students frequently
purchase, with prices ranging from 10 to 15 million VND. To minimize shortages, dis-
tributors should concentrate on these two important brands.
The findings of the association rule highlight some aspects of various majors:
About the brand
The majority of Foreign Trade students utilize Apple products; Students of MIS like
the HP brands, which cost 15-20 million VND; Students of Accounting and Business
choose Asus and Acer brands with prices under 10 million VND.
About the factors
Foreign Trade students are interested in design and security factors; MIS students are
interested in configuration and processing speed factors; Accounting, Business and
Commerce students are interested in price and promotion factors.
4.2 Sources of information for purchasing a laptop
Improve image promotion, coverage and word-of-mouth marketing: Use the credibility
of celebrities and loyal customer groups.
Enhance product quality and the company’s reputation; develop after-sales customer
care plans to encourage customers.
- Organize the hiring and training of professional staff who are familiar with laptops
and have a professional service style. Training and coaching staff to always smile at
customers in all situations.
- Create a separate customer service department to handle customer feedback. All re-
quests for purchasing, selling and delivering services are handled in a timely manner.
Companies can also set up separate phone lines to handle customer inquiries.
4.3 Programs for advertising and marketing
Companies must improve their communication on social networking sites, as well as
websites that combine programs, fairs, and exhibitions held at schools. Updating infor-
mation on e-commerce channels to disseminate knowledge and general information
about product features.
According to the clustering results, students are subjects with a limited budget who
place a high value on price and promotion. As a result, distribution and retail companies
should:
Applying Clustering Technique and Association Rule to Analyze Laptop Usage Behavior of
Students 73
- Implement a marketing strategy suitable to students of different majors according
to their behaviours and characteristics.
- Offer optimal payment terms, such as implementing an installment policy, purchas-
ing first and paying later with a 0% interest rate.
- Put product discounts, promotions, and giveaways into action. Promotions must be
diverse and of high quality.
- There are policies in place to allow customers to return goods and receive refunds
in certain circumstances, such as when laptop malfunctions.
5 Conclusions and future work
The article studied the theory of clustering techniques and association rules for applying
these techniques to build data mining models to analyze the laptop usage behavior of
students. The results of the clustering technique analysis have clarified the outstanding
features of groups of students with similar characteristics; the association rules discov-
ered from the data help to understand the relationship and influence of factors influenc-
ing students’ choice to buy laptops. The knowledge extracted from the models assists
laptop distributors and retailers in understanding the trends and characteristics of stu-
dents, allowing them to develop effective business strategies. More data will be gath-
ered in the coming months from a variety of sources, including not only students from
University of Economics-The University of Danang, but also extensive research with
students from all over Danang City, making the data more complete, in order to improve
the model and increase the efficiency of analysis and prediction.
References
1. Chuc, N.V. and Giang, D.T.: Applying clustering technique and association rule to exploit
data of customers using hotel services. Journal of science and technology (JST-UD),
12(97), 109-112 (2015).
http://tapchikhcn.udn.vn/volume/135/Ung_dung_ky_thuat_phan_cum_va_luat_ket_hop_k
hai_pha_du_lieu_khach_hang_su_dung_dich_vu_khach_san-8501/8501
2. Tan, P.N, Steinbach M., Karpatne, A., Kumar V.: Introduction to data mining, 2 nd Edition,
Pearson (2018).
3. Witten, L.H, Frank, E., Hall, M.A., Pal, C.J.: Data mining: Practical machine learning tools
and techniques, 4th Edition, Morgan Kaufmann (2016).
4. SQL Server Analysis Services (SSAS). https://docs.microsoft.com/en-us/analysis-ser-
vices/data-mining/data-mining-ssas?view=asallproducts-allversions.
5. Data Mining and Business Intelligence solution. http://bis.net.vn/forums/36.aspx