USING SIMILARITY DEGREES TO IMPROVE FUZZY MINING ASSOCIATION RULE BASED MODEL FOR ANALYZING IT ENTREPRENEURIAL TENDENCY

Endang Supriyati; Mohammad  Iqbal; Tutik Khotimah

doi:10.31436/iiumej.v20i2.1096

Authors

Endang Supriyati Universitas Muria Kudus https://orcid.org/0000-0003-0386-582X
Mohammad Iqbal https://orcid.org/0000-0002-7102-0629
Tutik Khotimah https://orcid.org/0000-0003-2516-3431

DOI:

https://doi.org/10.31436/iiumej.v20i2.1096

Keywords:

Association Rule, Fuzzy Mining, Similarity Degrees

Abstract

Higher education has great potential in producing new startups in the IT (Information Technology) field. Many choices influence students to become IT- entrepreneurs. Association Rule can be used to obtain a model by analysing data so that it can be used to make a rule to the IT entrepreneurship-student model, but the association algorithm has disadvantages in handling large datasets. We propose reducing candidate itemsets using degrees of fuzzy similarity. The membership function in fuzzy sets can be used to measure the quality of rules obtained. The purpose of this study is to improve the algorithm by evaluating the similarity of candidate itemsets to get a good quality rule. This research method has 2 phases, namely (1) calculating the membership function with similarity itemset and (2) applying fuzzy mining association rule. Phase 1 has several steps, including: preparation of a transaction database, the taxonomy process, and identification of similar itemset. Phase 2 has several steps as well. The first is defining membership functions, and the last is a fuzzy mining fuzzy association rule. In this study, a questionnaire was distributed to 1225 students who were members of the IT entrepreneurship program. The results of this study were reduced into 823 itemsets and produced an IT entrepreneurship rule model.

ABSTRAK: Pendidikan tinggi mempunyai potensi besar dalam menghasilkan permulaan baru dalam bidang IT. Banyak pilihan mempengaruhi pelajar bagi menjadi usahawan-IT. Kaedah Bersekutu boleh digunakan bagi mendapatkan model dengan menganalisa data supaya ianya dapat digunakan menjadi model kepada pelajar keusahawanan-IT, namun algoritma bersekutu mempunyai kelemahan dalam mengendalikan dataset yang besar. Kami mencadangkan pengurangan bilangan set item menggunakan tahapan persamaan kabur. Fungsi ahli dalam set kabur dapat digunakan bagi mengukur kualiti aturan yang diperoleh. Tujuan kajian ini adalah bagi meningkatkan algoritma dengan menilai persamaan set item calon bagi mendapatkan aturan kualiti yang baik. Kaedah penyelidikan ini mempunyai 2 peringkat, iaitu (1) mengira fungsi ahli dengan set item persamaan dan (2) menerapkan aturan perlombongan bersekutu kabur. Peringkat 1 mempunyai beberapa langkah, iaitu: urus niaga pangkalan data, proses taksonomi, identifikasi set item yang sama. Tahap 2 mempunyai beberapa langkah, iaitu: menentukan fungsi keahlian, dan akhirnya, aturan perlombongan bersekutu. Dalam kajian ini, soal selidik telah diedarkan kepada 1225 pelajar yang menjadi ahli program keusahawanan IT. Dapatan kajian menunjukkan pengurangan nombor dataset kepada 823 set item dan menghasilkan model aturan teknologi keusahawanan IT.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Lin, X. (2014) Mr-apriori: Association rules algorithm based on mapreduce. In Software Engineering and Service Science (ICSESS), 2014 5th IEEE International Conference on., pp. 141-144.

Kumar KS, Chezian RM. (2012) A survey on association rule mining using apriori algorithm. International Journal of Computer Applications (IJCA), pp. 47-50.

Patel B, Chaudhari VK, Karan RK, Rana YK. (2011) Optimization of association rule mining apriori algorithm using ACO. International Journal of Soft Computing and Engineering, Vol.1, No.1. pp 24-26.

Al-Maolegi M, Arkok B. (2014) An improved apriori algorithm for association rules. International Journal on Natural Language Computing (IJNLC) Vol.3, No.1. pp 21-29.

Kaur J, Madan N. (2015) Association rule mining: A survey. International Journal of Hybrid Information Technology, Vol.8, No.7. pp 239-242.

Kumar CP, Anjaiah P, Patil S, Lingappa E, Rakesh M. (2017) Mining Association Rules from No-SQL data bases using Map-Reduce Fuzzy Association Rule Mining Algorithm. International Journal of Applied Engineering Research, Vol.12, No.2. pp. 10472-10476.

Yuan, X. (2017). An improved Apriori algorithm for mining association rules. In AIP Conference Proceedings, pp 1-6.

Helm BL, Hahsler PDDM. (2007) Fuzzy Association Rules. Vienna University of Economics and Business Administration.

Verma SK, Thakur RS. (2017) Fuzzy Association Rule Mining based Model to Predict Students' Performance. International Journal of Electrical & Computer Engineering. Vol.3, No.4. pp.2223-2231

Moustafa A, Abuelnasr B, Abougabal MS. (2015) Efficient mining fuzzy association rules from ubiquitous data streams. Alexandria Engineering Journal, 54(2): 163-174.

Imran A, Vladimir K, Viktor L, Olga M. (2017) Fuzzy methods and algorithms in data mining and formation of digital plan-schemes in earth remote sensing. Procedia computer science, 120:120-125.

Johanyak ZC, Kovács S. (2005) Distance based similarity measures of fuzzy sets. Proceedings of SAMI,pp 1-12

Liao H, Xu Z, Zeng XJ. (2014) Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Information Sciences, 271: 125-142.

Liu HW. (2005) New similarity measures between intuitionistic fuzzy sets and between elements. Mathematical and Computer Modelling, 42(1-2): 61-70.

Wong KW, Gedeon T, Tikk D. (2000) An improved multidimensional alpha-cut based fuzzy interpolation technique, International Conference on Artificial in Science and Technology, pp 1-6.

Mendel JM. (2017) Type-2 fuzzy sets. In Uncertain Rule-Based Fuzzy Systems. Springer, pp. 259-306.

Wrapper H, Meijer H. (1997) A taxonomy for computer science. Technical Report CSI-R9713

Sujatha R, Bandaru R, Rao R (2011) Taxonomy construction techniques –issues and challenges. Indian Journal of Computer Science and Engineering,Vol.2, No.5. pp 661-671.

Ibrahim A. (2004) Fuzzy logic for embedded systems applications. Elsevier Science.

Salatino AA, Thanapalasingam T, Mannocci A, Osborne F, Motta E. (2018) The computer science ontology: a large-scale taxonomy of research areas. In International Semantic Web Conference, Springer, pp. 187-205.

Masapanta-Carrión S, Velázquez-Iturbide J Á. (2018) A Systematic Review of the Use of Bloom's Taxonomy in Computer Science Education. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education, pp. 441-446.

Dimapilis H. (2013) Are we ready for Technopreneurhip? A Study on selected local Entrepreneur within the city. Research Congress, De La Salle, Universty Manila, pp:1-7.

Amante AD, Ronquillo TA. (2017) Technopreneurship as an outcomes-based education tool applied in some engineering and computing science programme. Australasian Journal of Engineering Education, 22(1): 32-38

Hong TP, Lee YC, Wu MT. (2014) An Effective Parallel Approach for Genetic-Fuzzy Data Mining. Expert Systems with Applications, 41(2): 655-662.

Michael H. (2005) Applied Fuzzy Aritmatic An Introdution With Engineering Applications. Springer.

Cho M, Song M, Yoo S. (2014) A Systematic Methodology for Outpatient Process Analysis Based on Process Mining. In Asia-Pacific Conference on Business Process Management, Springer, pp. 31-42.