The Optimum Clustering Framework: Implementing the Cluster Hypothesis. Information retrieval and data analysis.
Семинар пройдёт в рамках цикла «Информационный поиск и анализ данных», на котором выступит специалист из Технического Университета Дармштадта. Задать вопросы докладчику и обсудить заявленную тему вы можете в клубе. Докладчик будет читать на английском языке.
Докладчик
Norbert Fuhr – studied technical computer science and received a PhD (Dr.-Ing.) from the Technical University of Darmstadt, Germany in 1986. He became associate professor at the University of Dortmund in 1991. Since 2002, he is full professor at the University of Duisburg-Essen, Germany. His current research interests are information retrieval models, user interfaces for information systems, and their evaluation.
Ключевые вопросы
In this talk, we present a theoretic foundation for optimum document clustering. Key idea is to base cluster analysis and evaluation on a set of queries, by defining documents as being similar if they are relevant to the same queries. Three components are essential within our optimum clustering framework, OCF: (1) a set of queries, (2) a probabilistic retrieval method, and (3) a document similarity metric.
After introducing an appropriate validity measure, we define optimum clustering with respect to the estimates of the relevance probability for the query-document pairs under consideration. Moreover, we show that well-known clustering methods are implicitly based on the three components, but that they use heuristic design decisions for some of them. We argue that with our framework, more targeted research for developing better document clustering methods becomes possible. Experimental results demonstrate the potential of our considerations.
