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

Abstract

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 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.

Analysis of the Impact of Residential Property and Equipment on Building Energy Efficiency and Consumption—A Data Mining Approach

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