Extracting the Significant Degrees of Attributes in Unlabeled Data using Unsupervised Machine Learning


Byoung Jik Lee, Western Illinois University, USA


We propose a valid approach to find the degree of important attributes in unlabeled dataset to improve the clustering performance. The significant degrees of attributes are extracted through the training of unsupervised simple competitive learning with the raw unlabeled data. These significant degrees are applied to the original dataset and generate the weighted dataset reflected by the degrees of influentialvalues for the set ofattributes. This work is simulated on the UCI Machine Learning repository dataset. The Scikit-learn K-Means clustering with raw data, scaled data, and the weighted data are tested. The result shows that the proposed approach improves the performance.


Unsupervised MachineLearning, Simple Competitive Learning, SignificantDegree of Attributes, Scikitlearn K-Means Clustering, Weighted Data, UCI Machine Learning Data.

Full Text  Volume 10, Number 16