Generating, Visualizing, and Evaluating High-Quality Clusters for Information Organization

We present and analyze the star clustering algorithm. We discuss an implementation of this algorithm that supports browsing and document retrieval through information organization. We define three parameters for evaluating a clustering algorithm to measure the topic separation and topic aggregation achieved by the algorithm. In the absence of benchmarks, we present a method for randomly generating clustering data. Data from our user study shows evidence that the star algorithm is effective for organizing information. 1 Introduction Modern information systems have vast amounts of unorganized data. Users often don't know what they need until they need it. In dynamic, time-pressured situations such as emergency relief for weather disasters, presenting the results of a query as a ranked list of hundreds of titles is ineffective. To cull the critical information out of a large set of potentially useful sources we need methods for organizing as accurately as possible the data and ways of v..