III: Small: Exploring Data in Multiple Clustering Views

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。本研究的主要目标是为聚类的新范式制定一个框架：从数据中发现所有可能的非冗余多聚类视图。典型的聚类算法只能找到一个聚类解，但许多真实和复杂的数据本质上是多方面的。数据可以用许多不同的方式来解释。对于相同的数据，医生感兴趣的内容与保险机构认为重要的内容是不同的。这项研究将为探索性数据分析提供新的公式、算法和工具，广泛适用于许多领域。PI将应用新算法检测皮肤病变，包括癌症/这将涉及皮肤图像中真皮和表皮连接的自动分割，机器声音的自动检测，以及开发文本和自然图像的多个非冗余聚类算法。此外，该项目将为本科生和研究生在课堂和实验室提供研究经验。PI将与女性工程师协会合作，鼓励女学生从事工程和计算机科学方面的职业，并确保代表性不足的群体参与这项研究。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The primary objective of this research is to formulate a framework for a new paradigm for clustering: discovering all possible non-redundant multiple clustering views from data. Typical clustering algorithms only find one clustering solution, but many real and complex data are multi-faceted by nature. Data can be interpreted in many different ways. Given the same data, what is interesting to a physician will be different from what is important to an insurance agency. This research will provide new formulations, algorithms, and tools for exploratory data analysis that are widely applicable to many domains. The PI will apply the new algorithms in detection of skin lesions, including cancers/ This will involve the automatic segmentation of the dermis and epidermis junction in skin images, automated detection of machine sounds, and in developing algorithms for multiple non-redundant clustering of text and natural images. Additionally, this project will provide research experiences for both undergraduate and graduate students in the classroom and in the lab. The PI will work with the Society of Women Engineers to inspire female students to pursue careers in engineering and computer science, and to ensure that under-represented groups are involved in this research.