A COMPARISON BETWEEN K-MEANS AND TREE ALGORITHM IN R SUPPORT VECTOR OF DATA MINING CLASSIFICATION PROBLEMS

Abdelatti  A Blg; Muner   Athaba; Sana   Abouljam; Abdalla Mohamed   Alasoud

doi:10.59743/jbs.v33i2.189

المؤلفون

Abdelatti A Blg Computer Science Department, Faculty of Science - Ajaylat, University of Zawia
Muner Athaba Computer Science Department, Faculty of Science - Ajaylat, University of Zawia
Sana Abouljam Computer Science Department, Faculty of Science - Ajaylat, University of Zawia
Abdalla Mohamed Alasoud Computer Department, Faculty of Science, Al-Asmarya Islamic University

DOI:

https://doi.org/10.59743/jbs.v33i2.189

الكلمات المفتاحية:

K-MEANS، Data Mining، Machine Learning، KNIME، Tree algorithm، R

الملخص

Correct analysis of the data to create a logical relationship that summarizes the data in a new way that is understandable and useful, the data will yield huge benefits. The process of analyzing and transforming data into knowledge is called knowledge discovery in the database. In the various steps of knowledge discovery in the database, the richness and precision of the algorithms make it difficult. When analyzing big data, effective user support is essential, and it is even more important now. Metadata is a necessary component to enhance user support [1]. In this paper, we will address problemsof data mining classification, four machine learning validation methods used to build and test 4 distinct datasets with the same amount of training and testing data for each predictive model. Besides, calculated the accuracy average and standard deviation of 20 trials, visualized the accuracy.

المراجع

. Bilalli, B., Abelló, A., Aluja-Banet, T. and Wrembel, R., 2016, April. Towards Intelligent Data Analysis: The Metadata Challenge. In IoTBD (pp. 331-338).

. Govender, P. and Sivakumar, V., 2020. Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019). Atmospheric Pollution Research, 11(1), pp.40-56.

. David, S.K., Saeb, A.T., Rafiullah, M. and Rubeaan, K., 2019. Classification Techniques and Data Mining Tools Used in Medical Bioinformatics. In Big Data Governance and Perspectives in Knowledge Management (pp. 105-126). IGI Global.

. Brijain, M., Patel, R., Kushik, M. and Rana, K., 2014. A survey on decision tree algorithm for classification.

. Liao, T.W., 2005. Clustering of time series data—a survey. Pattern recognition, 38(11), pp.1857-1874.

. Dubes, R. and Jain, A.K., 1976. Clustering techniques: the user's dilemma. Pattern Recognition, 8(4), pp.247-260.

. Sanakal Ravi and Jayakumari T, 2014. Prognosis of Diabetes Using Data Mining Approach-Fuzzy C Means Clusteringand Support Vector Machine. International Journal of Computer Trends and Technology.

. Sharma Arvind and Gupta PC, 2012. Predicting the Number of Blood Donors through their Age and Blood Group by using Data Mining Tool. International Journal of Communication and Computer Technologies. (01),pp.6-10

. Salama GI, Abdelhalim MB, Zeid MA 2012. Experimental comparison of classification for breast cancer diagnosis. International Conference on Computer Engineering &Systems. pp, (98-180-5)