Abstract
Image mining is now a thriving and expanding field of computer science research. Image mining is linked to the advancement of data mining in image preparation. Image mining is used to extract hidden information and in other situations where the photos do not clearly describe the situation. Image mining combines machine learning, data handling, application autonomy, and image preparation concepts. Semantic maps are used to visualize image data stored in image databases. We recommend using Multi-Child Semantic Maps to build semantic maps which fully display the image. In this study, we propose two path clustering on Multi-Child Semantic Maps (MCSM) using the K-C Means Clustering Algorithm, also known as the MCSMK-C algorithm. This algorithm causes image clustering and instructs the mining system to look at the image's top area. When mining, the MCSMK-C algorithm considers the X and Y coordinates. The system looks for groups by examining each object's territory in the database, and it saves a region if it contains more objects than the required number.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal on Recent and Innovation Trends in Computing and Communication
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
