Towards a taxonomy for computer science

Wupper, H; Meijer, Hans

doi:10.1007/978-0-387-35166-7_23

Cited by 3 publications

(2 citation statements)

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“…The selection of will have an effect on the results. The selection of the appropriate is based on the previous experience of the decision maker and/or the case under study [15][16][17] Association algorithms are used to analyse data to obtain a model, but association algorithms have disadvantages in handling large datasets. We propose reducing candidate set items using degrees of fuzzy similarity.…”

Section: Introductionmentioning

confidence: 99%

Using Similarity Degrees to Improve Fuzzy Mining Association Rule Based Model for Analyzing It Entrepreneurial Tendency

Supriyati

Iqbal²,

Khotimah³

2019

IIUMEJ

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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.

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Section: Introductionmentioning

confidence: 99%

Using Similarity Degrees to Improve Fuzzy Mining Association Rule Based Model for Analyzing It Entrepreneurial Tendency

Supriyati

Iqbal²,

Khotimah³

2019

IIUMEJ

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“…This project started with an existing diagram shown in Figure 5.1, created in this project initiators' informal discussions and inspired by a similar diagram proposed by (Wupper & Meijer 1997). The diagram outlines that the model represents the system with respect to the system requirement.…”

Section: Two Conceptual Modelsmentioning

confidence: 99%

Systematic modelling of embedded systems: managing non-formal aspects

Marinčić¹

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Many years ago, while studying electrical engineering, I have never dreamed that models and modelling would have become a "red thread" in my career. Back then, I preferred to see the world described with formulae (which are also models of a kind). When professors presented their findings using block diagrams, I found these descriptions unattractive and wanted to see more details. However, as soon as I started working as an embedded software engineer and faced the complexity of real systems, stakeholder needs and different mental models of different engineers, I recognised the usefulness of abstraction and of different kinds of (informal) models. I started to use and never stopped, all sorts of different kinds of (software) models to communicate, design, verify and validate.Through this thesis I was able to reflect on the process of modelling and perform research on topics I had already observed in practice, for example, how important it is not to forget what the purpose of a model is or to make sure that all the model reviewers have the same understanding of the problem. I am grateful to my promotor, Roel Wieringa and co-promotor, Angelika Mader, for taking me on this journey with them.Furthermore, I am grateful to colleagues both in research and practice who have been sharing the enthusiasm for modelling techniques and tools and thinking about the process, reflecting what we are doing while modelling and why. Sometimes, in practice, when the goal is to deliver a product, there is no time for this kind of reflection. When we make the time for it, our models and designs become better.Last but not least, I would not have been able to do this work without the support of my family and my circle of close friends that feel like family. Thank you so much! 11 12 11 Conclusion and discussion 11.

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