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Initial Seed Value Effectiveness on Performances of Data Mining Algorithms

Year 2021, Volume 9, Issue 2, 555 - 567, 25.04.2021
https://doi.org/10.29130/dubited.813101

Abstract

After 2000s, Computer capacities and features are increased and access to data made easy. However, the produced and recorded data should be meaningful. Transformation of unprocessed data into meaningful information can be done with the help of data mining. In this study, classification methods from data mining applications are studied. First, the parameters that make the results of the same data set different were investigated on 4 different data mining tools (Weka, Rapid Miner, Knime, Orange), It has been tested with 3 different algorithms (K nearest neighborhood, Naive Bayes, Random Forest). In order to evaluate the performance of the data set while creating the classification models, the data set was divided into training data and test data as 80% -20%, 70% -30% and 60-40%. The accuracy, roc and precision values was used to test the performance of the classifying data. While classifying, the effect of algorithm parameters on the results is observed. The most important of these parameters is the initial seed value. The initial seed is a value using especially in classification algorithms that determines the initial placement of the data and directly affects the result. In this respect, it is very important to determine the initial seed value correctly. In this study, initial seed values between 0 and 100 were evaluated and it was shown that the classification could change the accuracy value approximately by 5%.

References

  • [1] M. S. Durmuş, “Veri kümeleme algoritmalarının performansları üzerine karşılaştırmalı bir çalışma,” M.S. thesis, Fen Bilimleri Enstitüsü, Pamukkale Üniversitesi, Denizli, 2005.
  • [2] Y.Farhang, “Face Extraction from Image based on K-Means Clustering Algorithms,” International Journal Of Advanced Computer Science And Applications, 8(9), 96-107,2017.
  • [3] H. Kaya, K. Köymen, “Veri madenciliği kavramı ve uygulama alanları,” Doğu Anadolu bölgesi araştırmaları Dergisi, 6(2), 159-164, 2008.
  • [4] Q. Chen, Y. Wan, X. Zhang, Y. Lei, J. Zobel, K. Verspoor, “Comparative analysis of sequence clustering methods for deduplication of biological databases,” Journal of Data and Information Quality (JDIQ), 9(3), 17, 2018.
  • [5] M. A. ALAN, “VERİ MADENCİLİĞİ VE LİSANSÜSTÜ ÖĞRENCİ VERİLERİ ÜZERİNE BİR UYGULAMA,”Dumlupinar University Journal of Social Science/Dumlupinar Üniversitesi Soysyal Bilimler Dergisi, (33), 2012.
  • [6] S. ÖZŞEN, R. Ceylan, “Comparison of AIS and fuzzy c-means clustering methods on the classification of breast cancer and diabetes datasets,” Turkish Journal of Electrical Engineering & Computer Sciences, 22(5), 1241-1254, 2014.
  • [7] G. Kayakutlu, I. Duzdar, E. Mercier-Laurent, B. Sennaroglu, “Intelligent association rules for innovative SME collaboration,” IFIP International Workshop on Artificial Intelligence for Knowledge Management, Springer, Cham, 150-164, 2014.
  • [8] A. M. Moawad, A. M. Gadallah, M. H. Kholief, “Fuzzy Ontology based Approach for Flexible Association Rules Mining,” Internatıonal Journal Of Advanced Computer Scıence And Applıcatıons, 8(5), 328-337, 2017.
  • [9] T. Pala, I. YÜCEDAĞ, H. Biberoğlu, “Association rule for classification of breast cancer patients,” Sigma, 8(2), 155-160, 2017.
  • [10] R. A. Shah, S. Asghar, “Privacy preserving in association rules using a genetic algorithm,” Turkish Journal of Electrical Engineering & Computer Sciences, 22(2), 434-450, 2014.
  • [11] I. C. Yeh, C. H. Lien, “The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients,” Expert Systems with Applications, 36(2), 2473-2480, 2009.
  • [12] A. Dhall, G. Sharma, R. Bhatt, G. M. Khan, “Adaptive digital makeup”, International Symposium on Visual Computing, pp. 728-736, Springer, Berlin, Heidelberg, 2009.
  • [13] E. J. Lauría, A. D. March, “Combining bayesian text classification and shrinkage to automate healthcare coding: A Data Quality Analysis,” Journal of Data and Information Quality (JDIQ), 2(3), 13, 2011.
  • [14] K. Rangra, K. L. Bansal, “Comparative study of data mining tools,” International journal of advanced research in computer science and software engineering, 4(6), 2014.
  • [15] F. BULUT, I. O. BUCAK, “An urgent precaution system to detect students at risk of substance abuse through classification algorithms,” Turkish Journal of Electrical Engineering & Computer Sciences, 22(3), 690-707, 2014.
  • [16] A. H. Wahbeh, Q. A. Al-Radaideh, M. N. Al-Kabi, E. M. Al-Shawakfa, “A comparison study between data mining tools over some classification methods,” International Journal of Advanced Computer Science and Applications, 8(2), 18-26, 2011.
  • [17] A. Tekerek, “Veri madenciliği süreçleri ve açık kaynak kodlu veri madenciliği araçları,” Akademik Bilişim, 11, 2-4, 2011.
  • [18] M. Dener, M. Dörterler, A. Orman, “Açık kaynak kodlu veri madenciliği programları: WEKA’da örnek uygulama,” Akademik Bilişim, 9, 11-13, 2009.
  • [19] E. Atagün, İ. D. Argun, “A Comparison of Data Mining Tools and Classification Algorithms: Content Producers on the Video Sharing Platform”, In The International Conference on Artificial Intelligence and Applied Mathematics in Engineering (pp. 526-538), Springer, Cham, 2019.
  • [20] Ş. E. Şeker, “İş zekası ve veri madenciliği,”in Cinius Yayınları, İstanbul, 2013.
  • [21] «WEKA,» 01.04.2020. [Online]. Available: http://www.cs.waikato.ac.nz/ml/weka/.
  • [22] M. Kaya, S. A. Özel, “Açık Kaynak Kodlu Veri Madenciliği Yazılımlarının Karşılaştırılması,” 14. Akademik Bilişim Konferansı, 5-7, 2014.
  • [23] M. Turanlı, U. H. Özden, S. Türedi, “Avrupa Birliği’ne aday ve üye ülkelerin ekonomik benzerliklerinin kümeleme analiziyle incelenmesi”. 2006. [Çevrimiçi]. Erişim Adresi: http://acikerisim.ticaret.edu.tr/xmlui/handle/11467/891#sthash.tFw7f06H.dpbs
  • [24] A. Tiwari, A. K. Sekhar, “Workflow based framework for life science informatics,” Computational biology and chemistry, 31(5-6), 305-319, 2007.
  • [25] «KNIME,» 01.04.2020. [Online]. Available: http://www.knime.org/.
  • [26] «ORANGE,» 01.04.2020. [Online]. Available: http://orange.biolab.si/.
  • [27] «RAPIDMINER,» 01.04.2020. [Online]. Available: http://www.rapidminer.com/.
  • [28] «Wikipedia,»08.05.2019[Online].Available: http://en.wikipedia.org/wiki/Naive_Bayes_classifier.
  • [29] «Wikipedia,» 08.05.2019 [Online]. Available: https://en.wikipedia.org/wiki/Randomforest
  • [30] «Towards Data Science,» 08.05.2019 [Online]. Available: https://towardsdatascience.com/the-random-forest-algorithm-d457d499ffcd
  • [31] J. R. Quinlan, “Simplifying decision trees,” International journal of man-machine studies, 27(3), 221-234, 1987.
  • [32] L. Mason, P. L. Bartlett, J. Baxter, “ Direct optimization of margins improves generalization in combined classifiers,” Advances in neural information processing systems, pp. 288-294, 1999.
  • [33] D. Dua, E. Karra Taniskidou, UCI Machine Learning Repository [http://archive.ics.uci.edu/ml], Irvine, CA: University of California, School of Information and Computer Science, 2017.
  • [34] A. Stetco, X. J. Zeng, J. Keane, “Fuzzy C-means++: Fuzzy C-means with effective seeding initialization,” Expert Systems with Applications, 42(21), 7541-7548, 2015.
  • [35] M. A. Rahman, M. Z. Islam, “Application of a density based clustering technique on biomedical datasets,” Applied Soft Computing, 73, 623-634, 2018.
  • [36] M. A. Mahdi, S. E. Abdelrahman, R. Bahgat, “A High-Performing Similarity Measure for Categorical Dataset with SF-Tree Clustering Algorithm,” International Journal Of Advanced Computer Science And Applications, 9(5), 496-509, 2018.
  • [37] P. ELIASSON, N. Rosen, “Efficient K-means clustering and the importance of seeding,” 2013.

Veri Madenciliği Algoritmalarının Performanslarında İlk Tohum Değer Etkinliği

Year 2021, Volume 9, Issue 2, 555 - 567, 25.04.2021
https://doi.org/10.29130/dubited.813101

Abstract

2000'li yıllardan sonra, Bilgisayar kapasiteleri ve özellikleri artmış ve verilere erişim kolaylaşmıştır. Ancak üretilen ve kaydedilen veriler anlamlı olmalıdır. İşlenmemiş verilerin anlamlı bilgilere dönüştürülmesi, veri madenciliği yardımı ile yapılabilmektedir. Bu çalışmada, veri madenciliği uygulamalarından sınıflandırma yöntemleri incelenmiştir. Öncelikle aynı veri setinin sonuçlarını farklı kılan parametreler 4 farklı veri madenciliği aracında (Weka, Rapid Miner, Knime, Orange) araştırılmış, 3 farklı algoritma ile test edilmiştir (K nearest neighborhood, Naive Bayes, Random Forest). Sınıflandırma modelleri oluşturulurken veri setinin performansını değerlendirmek için veri seti eğitim verileri ve test verileri olarak % 80-% 20, % 70-% 30 ve% 60-40 olarak ayrılmıştır. Accuracy, roc and precision değerleri, sınıflandırma verilerinin performansını test etmek için kullanılmıştır. Sınıflandırma yapılırken algoritma parametrelerinin sonuçlar üzerindeki etkisi gözlemlenmiştir. Bu parametrelerden en önemlisi ilk tohum değeridir. İlk tohum, özellikle verilerin ilk yerleşimini belirleyen ve sonucu doğrudan etkileyen sınıflandırma algoritmalarında kullanılan bir değerdir. Bu açıdan ilk tohum değerinin doğru belirlenmesi çok önemlidir. Bu çalışmada 0 ile 100 arasındaki başlangıç tohum değerleri değerlendirilmiş ve sınıflandırmanın doğruluk değerini yaklaşık %5 değiştirebileceği gösterilmiştir.

References

  • [1] M. S. Durmuş, “Veri kümeleme algoritmalarının performansları üzerine karşılaştırmalı bir çalışma,” M.S. thesis, Fen Bilimleri Enstitüsü, Pamukkale Üniversitesi, Denizli, 2005.
  • [2] Y.Farhang, “Face Extraction from Image based on K-Means Clustering Algorithms,” International Journal Of Advanced Computer Science And Applications, 8(9), 96-107,2017.
  • [3] H. Kaya, K. Köymen, “Veri madenciliği kavramı ve uygulama alanları,” Doğu Anadolu bölgesi araştırmaları Dergisi, 6(2), 159-164, 2008.
  • [4] Q. Chen, Y. Wan, X. Zhang, Y. Lei, J. Zobel, K. Verspoor, “Comparative analysis of sequence clustering methods for deduplication of biological databases,” Journal of Data and Information Quality (JDIQ), 9(3), 17, 2018.
  • [5] M. A. ALAN, “VERİ MADENCİLİĞİ VE LİSANSÜSTÜ ÖĞRENCİ VERİLERİ ÜZERİNE BİR UYGULAMA,”Dumlupinar University Journal of Social Science/Dumlupinar Üniversitesi Soysyal Bilimler Dergisi, (33), 2012.
  • [6] S. ÖZŞEN, R. Ceylan, “Comparison of AIS and fuzzy c-means clustering methods on the classification of breast cancer and diabetes datasets,” Turkish Journal of Electrical Engineering & Computer Sciences, 22(5), 1241-1254, 2014.
  • [7] G. Kayakutlu, I. Duzdar, E. Mercier-Laurent, B. Sennaroglu, “Intelligent association rules for innovative SME collaboration,” IFIP International Workshop on Artificial Intelligence for Knowledge Management, Springer, Cham, 150-164, 2014.
  • [8] A. M. Moawad, A. M. Gadallah, M. H. Kholief, “Fuzzy Ontology based Approach for Flexible Association Rules Mining,” Internatıonal Journal Of Advanced Computer Scıence And Applıcatıons, 8(5), 328-337, 2017.
  • [9] T. Pala, I. YÜCEDAĞ, H. Biberoğlu, “Association rule for classification of breast cancer patients,” Sigma, 8(2), 155-160, 2017.
  • [10] R. A. Shah, S. Asghar, “Privacy preserving in association rules using a genetic algorithm,” Turkish Journal of Electrical Engineering & Computer Sciences, 22(2), 434-450, 2014.
  • [11] I. C. Yeh, C. H. Lien, “The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients,” Expert Systems with Applications, 36(2), 2473-2480, 2009.
  • [12] A. Dhall, G. Sharma, R. Bhatt, G. M. Khan, “Adaptive digital makeup”, International Symposium on Visual Computing, pp. 728-736, Springer, Berlin, Heidelberg, 2009.
  • [13] E. J. Lauría, A. D. March, “Combining bayesian text classification and shrinkage to automate healthcare coding: A Data Quality Analysis,” Journal of Data and Information Quality (JDIQ), 2(3), 13, 2011.
  • [14] K. Rangra, K. L. Bansal, “Comparative study of data mining tools,” International journal of advanced research in computer science and software engineering, 4(6), 2014.
  • [15] F. BULUT, I. O. BUCAK, “An urgent precaution system to detect students at risk of substance abuse through classification algorithms,” Turkish Journal of Electrical Engineering & Computer Sciences, 22(3), 690-707, 2014.
  • [16] A. H. Wahbeh, Q. A. Al-Radaideh, M. N. Al-Kabi, E. M. Al-Shawakfa, “A comparison study between data mining tools over some classification methods,” International Journal of Advanced Computer Science and Applications, 8(2), 18-26, 2011.
  • [17] A. Tekerek, “Veri madenciliği süreçleri ve açık kaynak kodlu veri madenciliği araçları,” Akademik Bilişim, 11, 2-4, 2011.
  • [18] M. Dener, M. Dörterler, A. Orman, “Açık kaynak kodlu veri madenciliği programları: WEKA’da örnek uygulama,” Akademik Bilişim, 9, 11-13, 2009.
  • [19] E. Atagün, İ. D. Argun, “A Comparison of Data Mining Tools and Classification Algorithms: Content Producers on the Video Sharing Platform”, In The International Conference on Artificial Intelligence and Applied Mathematics in Engineering (pp. 526-538), Springer, Cham, 2019.
  • [20] Ş. E. Şeker, “İş zekası ve veri madenciliği,”in Cinius Yayınları, İstanbul, 2013.
  • [21] «WEKA,» 01.04.2020. [Online]. Available: http://www.cs.waikato.ac.nz/ml/weka/.
  • [22] M. Kaya, S. A. Özel, “Açık Kaynak Kodlu Veri Madenciliği Yazılımlarının Karşılaştırılması,” 14. Akademik Bilişim Konferansı, 5-7, 2014.
  • [23] M. Turanlı, U. H. Özden, S. Türedi, “Avrupa Birliği’ne aday ve üye ülkelerin ekonomik benzerliklerinin kümeleme analiziyle incelenmesi”. 2006. [Çevrimiçi]. Erişim Adresi: http://acikerisim.ticaret.edu.tr/xmlui/handle/11467/891#sthash.tFw7f06H.dpbs
  • [24] A. Tiwari, A. K. Sekhar, “Workflow based framework for life science informatics,” Computational biology and chemistry, 31(5-6), 305-319, 2007.
  • [25] «KNIME,» 01.04.2020. [Online]. Available: http://www.knime.org/.
  • [26] «ORANGE,» 01.04.2020. [Online]. Available: http://orange.biolab.si/.
  • [27] «RAPIDMINER,» 01.04.2020. [Online]. Available: http://www.rapidminer.com/.
  • [28] «Wikipedia,»08.05.2019[Online].Available: http://en.wikipedia.org/wiki/Naive_Bayes_classifier.
  • [29] «Wikipedia,» 08.05.2019 [Online]. Available: https://en.wikipedia.org/wiki/Randomforest
  • [30] «Towards Data Science,» 08.05.2019 [Online]. Available: https://towardsdatascience.com/the-random-forest-algorithm-d457d499ffcd
  • [31] J. R. Quinlan, “Simplifying decision trees,” International journal of man-machine studies, 27(3), 221-234, 1987.
  • [32] L. Mason, P. L. Bartlett, J. Baxter, “ Direct optimization of margins improves generalization in combined classifiers,” Advances in neural information processing systems, pp. 288-294, 1999.
  • [33] D. Dua, E. Karra Taniskidou, UCI Machine Learning Repository [http://archive.ics.uci.edu/ml], Irvine, CA: University of California, School of Information and Computer Science, 2017.
  • [34] A. Stetco, X. J. Zeng, J. Keane, “Fuzzy C-means++: Fuzzy C-means with effective seeding initialization,” Expert Systems with Applications, 42(21), 7541-7548, 2015.
  • [35] M. A. Rahman, M. Z. Islam, “Application of a density based clustering technique on biomedical datasets,” Applied Soft Computing, 73, 623-634, 2018.
  • [36] M. A. Mahdi, S. E. Abdelrahman, R. Bahgat, “A High-Performing Similarity Measure for Categorical Dataset with SF-Tree Clustering Algorithm,” International Journal Of Advanced Computer Science And Applications, 9(5), 496-509, 2018.
  • [37] P. ELIASSON, N. Rosen, “Efficient K-means clustering and the importance of seeding,” 2013.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Tunahan TİMUÇİN (Primary Author)
DÜZCE ÜNİVERSİTESİ
0000-0003-0332-4118
Türkiye


İrem DUZDAR ARGUN
DUZCE UNIVERSITY
0000-0002-7642-8121
Türkiye

Publication Date April 25, 2021
Published in Issue Year 2021, Volume 9, Issue 2

Cite

Bibtex @research article { dubited813101, journal = {Düzce Üniversitesi Bilim ve Teknoloji Dergisi}, eissn = {2148-2446}, address = {}, publisher = {Duzce University}, year = {2021}, volume = {9}, number = {2}, pages = {555 - 567}, doi = {10.29130/dubited.813101}, title = {Initial Seed Value Effectiveness on Performances of Data Mining Algorithms}, key = {cite}, author = {Timuçin, Tunahan and Duzdar Argun, İrem} }
APA Timuçin, T. & Duzdar Argun, İ. (2021). Initial Seed Value Effectiveness on Performances of Data Mining Algorithms . Düzce Üniversitesi Bilim ve Teknoloji Dergisi , 9 (2) , 555-567 . DOI: 10.29130/dubited.813101
MLA Timuçin, T. , Duzdar Argun, İ. "Initial Seed Value Effectiveness on Performances of Data Mining Algorithms" . Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9 (2021 ): 555-567 <https://dergipark.org.tr/en/pub/dubited/issue/61950/813101>
Chicago Timuçin, T. , Duzdar Argun, İ. "Initial Seed Value Effectiveness on Performances of Data Mining Algorithms". Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9 (2021 ): 555-567
RIS TY - JOUR T1 - Initial Seed Value Effectiveness on Performances of Data Mining Algorithms AU - Tunahan Timuçin , İrem Duzdar Argun Y1 - 2021 PY - 2021 N1 - doi: 10.29130/dubited.813101 DO - 10.29130/dubited.813101 T2 - Düzce Üniversitesi Bilim ve Teknoloji Dergisi JF - Journal JO - JOR SP - 555 EP - 567 VL - 9 IS - 2 SN - -2148-2446 M3 - doi: 10.29130/dubited.813101 UR - https://doi.org/10.29130/dubited.813101 Y2 - 2021 ER -
EndNote %0 Duzce University Journal of Science and Technology Initial Seed Value Effectiveness on Performances of Data Mining Algorithms %A Tunahan Timuçin , İrem Duzdar Argun %T Initial Seed Value Effectiveness on Performances of Data Mining Algorithms %D 2021 %J Düzce Üniversitesi Bilim ve Teknoloji Dergisi %P -2148-2446 %V 9 %N 2 %R doi: 10.29130/dubited.813101 %U 10.29130/dubited.813101
ISNAD Timuçin, Tunahan , Duzdar Argun, İrem . "Initial Seed Value Effectiveness on Performances of Data Mining Algorithms". Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9 / 2 (April 2021): 555-567 . https://doi.org/10.29130/dubited.813101
AMA Timuçin T. , Duzdar Argun İ. Initial Seed Value Effectiveness on Performances of Data Mining Algorithms. DUBİTED. 2021; 9(2): 555-567.
Vancouver Timuçin T. , Duzdar Argun İ. Initial Seed Value Effectiveness on Performances of Data Mining Algorithms. Düzce Üniversitesi Bilim ve Teknoloji Dergisi. 2021; 9(2): 555-567.
IEEE T. Timuçin and İ. Duzdar Argun , "Initial Seed Value Effectiveness on Performances of Data Mining Algorithms", Düzce Üniversitesi Bilim ve Teknoloji Dergisi, vol. 9, no. 2, pp. 555-567, Apr. 2021, doi:10.29130/dubited.813101