Knowledge-based Clustering, U.S.-Indo Collaboration Award in Indian Currency

9412209 Murty描述：该项目是为了支持密歇根州立大学计算机科学系的Anil K.Jain博士和印度班加罗尔印度科学研究所计算机科学与自动化系的M.Narasimha Murty博士的合作。这两位科学家将在基于知识的集群领域合作。研究的主要焦点将是以下主题：整合领域知识以获得更有意义的聚类；研究标记对象的分组以实现有效的表示和规则生成；以及开发工具以帮助潜在用户为给定问题选择适当的聚类算法。范围：集群分析试图通过将模式组织成组或集群来评估模式之间的相互作用，使得集群内的模式比属于不同集群的模式彼此更相似。这些结果可以用来启动关于数据的假设。这种方法在模式识别、计算机视觉、机器学习、生物学、精神病学、心理学、市场营销和信息检索中很受欢迎。这两位非常有能力的科学家的合作应该会促进所使用的技术，并导致更好地利用该方法。***

9412209 Murty Description: This project is to support collaboration by Dr. Anil K. Jain of the Department of Computer Science at Michigan State University, and Dr. M. Narasimha Murty of the Department of Computer Science and Automation at the Indian Institute of Science in Bangalore, India. The two scientists will work jointly in the area of knowledge-based clustering. The main focus of the research will be the following topics: incorporate domain knowledge to obtain more meaningful clusters; investigate grouping of labelled objects for efficient representation and rule generation; and develop tools to assist a potential user in selecting an appropriate clustering algorithm for a given problem. Scope: Cluster analysis attempts to assess the interaction among patterns by organizing the patterns into groups or clusters such that patterns within a cluster are more similar to each other than patterns belonging to different clusters. The results can be used to initiate hypotheses about the data. The method is popular in pattern recognition, computer vision, machine learning, biology, psychiatry, psychology, marketing, and information retrieval. The collaboration by these two highly competent scientists should advance the techniques used and lead to better utilization of the method. ***