Sharif University of Technology Scientia Iranica 1026-3098 27 5 2020 10 01 A novel selective clustering framework for appropriate labeling of clusters based on K-means algorithm 2621 2634 21265 10.24200/sci.2019.51110.2010 EN F. Moslehi School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran. A. Haeri School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran. M.R. Gholamian School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran. Journal Article 2018 05 26 Clustering is one of the main methods of data mining. K-means algorithm is one of the most common clustering algorithms due to its efficiency and ease of use. One of the challenges of clustering is to identify the appropriate label for each cluster. The selection of a label is done in such a way as to provide a proper description of the cluster records. In some cases, choosing the appropriate label is not easy due to the results and structure of each cluster. The aim of this study is to present an algorithm based on the K-means clustering in order to facilitate the allocation of labels to each cluster. Moreover, in many data mining issues, the data set contains a large number of fields and therefore, the identification of the fields and the extraction of subsets from the required fields is an important issue. With the help of the proposed algorithm, the important and influential variables of the data set would be identified and the subset of the required fields would be selected. https://scientiairanica.sharif.edu/article_21265_3de49640ed43a0c1cf8f9423319e8f81.pdf