In this paper we propose a hierarchical clustering engine, called SnakeT, that is able to organize on-the-fly the search results drawn from 16 commodity search engines into a hierarchy of labeled folders. The hierarchy o#ers a complementary view to the flat-ranked list of results returned by current search engines. Users can navigate through the hierarchy driven by their search needs. This is especially useful for informative, polysemous and poor queries.

Abstract — Finding trends in database data is hard when presented with data sets containing many attributes (columns). The difficulty is increased when the data is in text fields and may include large summary or remarks fields. This paper discusses an approach that uses attribute level clustering in order to discover trends or profiles in the data. This is different from traditional uses of clustering in that each attribute is clustered separately and then the results are combined to define profiles. For example, in a case study of the Global Terrorism Database (GTD) data set, there are 98 columns (attributes) in the data. A profile might be defined by a particular group, attack type, weapon type and by specific information found in larger remarks-type fields. The profiles will show the values of these attributes along with all the records that matched that profile.