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

本研究的目标是研究和开发针对聚类、模式识别、数据挖掘和图像处理问题的离散优化算法。这项研究将讨论这些离散优化技术的理论和实践，并将它们与传统的方法进行比较，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，其实际性能将在医学成像、安全检测和图像分割应用中进行评估。理论分析将解决几个不能有效和最优解决的聚类问题的算法性能。对于这种已知的困难问题，将根据所获得的解与最优解的接近程度或最坏情况的错误率来评估性能。将开发有效的实现来快速解决彼此略有不同的数据集序列上的模式识别和聚类问题(例如，对于动态变化的图像，例如视频中的图像)。这项研究的结果有望改进模式识别、图像分割、聚类和共分割的自动化或半自动化方法。预期的好处包括降低成本和减少图像分析中的人为错误频率。特别是，通过引入准确和快速的自动化程序，异常或病理特征的自动识别有望改善诊断并降低评估医学图像的成本。拟议方法的高速将允许实时部署，并可能有助于加快医疗保健、生物科学和国土安全应用程序的研究和开发速度。

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.