Users of Web search engines are often forced to sift through the long ordered list of document "snippets" returned by the engines. The IR community has explored document clustering as an alternative method of organizing retrieval results, but clustering has yet to be deployed on most major search engines. The NorthernLight search engine organizes its output into "custom folders" based on pre-computed document labels, but does not reveal how the folders are generated or how well they correspond to users' interests.In this paper, we introduce Grouper -an interface to the results of the HuskySearch meta-search engine, which dynamically groups the search results into clusters labeled by phrases extracted from the snippets. In addition, we report on the first empirical comparison of user Web search behavior on a standard ranked-list presentation versus a clustered presentation. By analyzing HuskySearch logs, we are able to demonstrate substantial differences in the number of documents followed, and in the amount of time and effort expended by users accessing search results through these two interfaces.

Abstract. Knowledge Discovery in Databases (KDD) focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured textual form. Previous work in text mining focused at the word or the tag level. This paper presents an approach to performing text mining at the term level. The mining process starts by preprocessing the document collection and extracting terms from the documents. Each document is then represented by a set of terms and annotations characterizing the document. Terms and additional higher-level entities are then organized in a hierarchical taxonomy. In this paper we will describe the Term Extraction module of the Document Explorer system, and provide experimental evaluation performed on a set of 52,000 documents published by Reuters in the years 1995-1996.

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TextVis is a visual data mining system for document collections. Such a collection represents an application domain, and the primary goal of the system is to derive patterns that provide knowledge about this domain. Additionally, the derived patterns can be used to browse the collection. TextVis takes a multi-strategy approach to text mining, and enables defining complex analysis schemas from basic components, provided by the system. An analysis schema is constructed by dragging functional icons from a tool-pallette onto the workspace and connecting them according to the desired flow of information.The system provides a large collection of basic analysis tools, including: frequent sets, associations, concept distributions, and concept correlations. The discovered patterns are presented in a visual interface allowing the user to operate on the results, and to access the associated documents. TextVis is a complete text mining system which uses agent technology to access various online information sources, text preprocessing tools to extract relevant information from the documents, a variety of data mining algorithms, and a set of visual browsers to view the results. This paper provides an overview on the TextVis system. We describe the system's architecture, the various tools, and discuss the advantages of our visual environment for mining large document collections.