New Clustering Algorithm Paves Path to Autonomous AI
Dr. Jie Yang and Distinguished Professor CT Lin's paper on the novel torque clustering method has been published in IEEE TPAMI, a leading journal in the field of artificial intelligence.
Dr Jie Yang and Distinguished Professor CT Lin from the Computational Intelligence and Brain Computer Interface Lab at AAII have developed a novel torque clustering algorithm as a competitive alternative to existing methods. This advancement makes a significant step toward autonomous AI and unsupervised learning. Their research, titled "Autonomous Clustering by Fast Find of Mass and Distance Peaks," has recently been published in the IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI).
The torque clustering algorithm is entirely parameter-free, allowing it to autonomously recognize various cluster types, determine the optimal number of clusters, and identify noise. Tested on 1,000 diverse datasets, it achieved an average adjusted mutual information score of 97.7%, outperforming other state-of-the-art methods, which typically score in the 80% range.
The new algorithm could also contribute to the development of general artificial intelligence by improving AI systems’ ability to learn and adapt without human intervention, as highlighted in the article "Scientists Build AI Model That Advances Artificial General Intelligence" published by AI Business.
Read more about the research in UTS Engineering and Information Technology News
