D. Shobana
Innovative approaches to efficient urban planning are required due to the fast pace of urbanization and the emergence of smart city initiatives. As a multidisciplinary analytical technique, data mining offers chances to find patterns, forecast trends, and produce useful insights in a variety of fields. The use of data mining methods into urban planning procedures is examined in this research. We show how association rule mining, clustering, and classification may optimize resource allocation, improve infrastructure, and enhance public services. The advantages of integrating data mining with knowledge from public health, environmental science, transportation, and socioeconomics are demonstrated through case studies of actual smart city initiatives. The study also discusses issues with privacy, data ethics, and the necessity of interdisciplinary cooperation. This study adds to the expanding corpus of research supporting a multidisciplinary, data-driven strategy
Data Mining, Smart Cities, Urban Planning, Multidisciplinary Approach, Bigdata, Sustainability, Public Services