A novel selective clustering framework for appropriate labeling of clusters based on K-means algorithm

Data mining is a powerful new technology to extract hidden information from data warehouses. Data mining analyzes data from different perspectives and finds useful patterns and knowledge from large volumes of raw data. 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 so as to provide a proper description of cluster records. In some cases, choosing an 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. (C) 2020 Sharif University of Technology. All rights reserved.