Abstract
Clustering is widely used in a variety of fields to find structures among data and extract useful knowledge. Recently, there has been an emergent need for robust and efficient techniques that can manage the exploding volume of data available in the World Wide Web or gathered from devices and sensors. However, clustering such data is challenging, due to the multimodal nature of this information. In this work, we introduce a novel Multimodal Adaptive Genetic Clustering (MAGC) algorithm that clusters information based on multiple features. Our approach adds feature weights as an extension to the chromosome, which represents a clustering solution, such that feature weights are also evolved and optimized alongside the original clustering solution. The number of clusters is also adaptive and is optimized during the search.