Collaborative Research: ITR: Models, Algorithms and Analyses for Clustering Data

合作研究：ITR：聚类数据的模型、算法和分析

• 批准号: 0312354
• 负责人: Ravindran Kannan
• 金额: $ 9万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-15 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0312354&HistoricalAwards=false
• 关键词: Collaborative; Research; ITR; Models; Algorithms

The unprecedented explosion in the volume of readily accessible data in recent years, although highly desirable, comes at a heavy price - it is now considerably more difficult to locate the answer to a specific question, or to find patterns and correlations that are central to a phenomenon. The PI's propose to study general-purpose clustering as a solution to the problem of locating relevant information and organizing it in an intelligible way. The proposal falls into two tracks. The first track is to develop measures and criteria that accurately re the quality of a clustering. The PI's have initiated work in this direction, and plan to build a theory of clustering criteria firmly grounded in the intuition of practitioners. The second track is the design of efficient algorithms to find near-optimal clustering under the right measures. The PI's propose to develop algorithms based on the so-called "spectral methods" and other techniques. They plan to make them efficient by the use of random (non-uniform) sampling. The PI's also plan to implement some of the algorithms and empirically evaluate them. The PI's propose to ensure that any positive impact from their research is maximized by (a) widely disseminating the results via conferences and also by inviting other researchers to discuss the ideas, (b) by making the preliminary implementations of clustering algorithms available.

近年来，易于访问的数据量出现了前所未有的爆炸式增长，尽管非常令人向往，但也付出了沉重的代价——现在要找到特定问题的答案，或者找到对某个问题至关重要的模式和相关性要困难得多。现象。 PI 建议研究通用聚类，作为定位相关信息并以可理解的方式组织信息的问题的解决方案。该提案分为两个轨道。第一条途径是制定准确评估聚类质量的措施和标准。 PI 已经开始朝这个方向开展工作，并计划建立一个牢固地扎根于实践者直觉的聚类标准理论。第二条路线是设计有效的算法，以在正确的措施下找到接近最优的聚类。 PI 建议开发基于所谓的“光谱方法”和其他技术的算法。他们计划通过使用随机（非均匀）抽样来提高效率。 PI 还计划实施一些算法并根据经验对其进行评估。 PI 建议通过以下方式确保其研究的积极影响最大化：(a) 通过会议广泛传播结果并邀请其他研究人员讨论这些想法，(b) 提供聚类算法的初步实现。