Novel Efficient Clustering Techniques for Data Mining, Ranking, Pattern Recognition and Segmentation of Large Scale Data Sets

用于大规模数据集的数据挖掘、排序、模式识别和分割的新型高效聚类技术

• 批准号: 1200592
• 负责人: Dorit Hochbaum
• 金额: $ 32.5万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1200592&HistoricalAwards=false
• 关键词: Novel; Efficient; Clustering; Techniques; Data

The goal of this research is to investigate and develop discrete optimization algorithms for problems of clustering, pattern recognition, data mining and image processing. The research will address the theory and practice of such discrete optimization techniques, and will compare them to traditional approaches including variational models, spectral analysis, support vector machines and Principal Component Analysis. Algorithms will be implemented and their practical performance will be evaluated in applications of medical imaging; security detection; and image segmentation. The theoretical analysis will address the performance of algorithms for several clustering problems, which cannot be solved efficiently and optimally. For such known hard problems the performance will be evaluated in terms of how close the solutions attained are to the optimum, or the worst case error ratio. Efficient implementations will be developed to solve quickly pattern recognition and clustering problems on a sequence of data-sets that differ slightly from each other (e.g. for dynamically changing images, as in video). The results of this research are expected to improve automated or semi-automated methodologies for pattern recognition, image segmentation, clustering and co-segmentation. The anticipated benefits include the reduction in cost and the frequency of human error in image analysis. In particular, automatic identification of unusual or pathological features is expected to improve diagnosis and reduce the cost of evaluating medical images by introducing accurate and fast automated procedures. The high speed of the proposed methodologies will permit real time deployment and mayl contribute to speeding up the rate of research and development in health-care, biological sciences and homeland security applications.

本研究的目标是研究和开发离散优化算法的聚类，模式识别，数据挖掘和图像处理的问题。该研究将解决这种离散优化技术的理论和实践，并将它们与传统方法进行比较，包括变分模型，谱分析，支持向量机和主成分分析。算法将被实现，并在医学成像的应用中评估其实际性能;安全检测;和图像分割。理论分析将解决几个聚类问题的算法的性能，这不能有效地解决和最佳。对于这些已知的困难问题，性能将根据所获得的解决方案与最佳或最坏情况错误率的接近程度进行评估。 将开发有效的实现，以快速解决模式识别和聚类问题的一系列数据集，彼此略有不同（例如，动态变化的图像，如视频）。 本研究的结果有望改善自动或半自动的方法模式识别，图像分割，聚类和联合分割。预期的好处包括降低成本和图像分析中人为错误的频率。特别是，异常或病理特征的自动识别有望通过引入准确和快速的自动化程序来改善诊断并降低评估医学图像的成本。 所提出的方法的高速度将允许真实的时间部署，并且可能有助于加速在卫生保健、生物科学和国土安全应用中的研究和开发的速率。