Abstract

We describe a system we developed for identifying trends in text documents collected over a period of time. Trends can be used, for example, to discover that a company is shifting interests from one domain to another. Our system uses several data mining techniques in novel ways and demonstrates a method in which to visualize the trends. We also give experiences from applying this system to the IBM Patent Server, a database of U.S. patents. Introduction We address the problem of discovering trends in text databases. We are given a database D of documents. Each document consists of one or more text fields and a timestamp. The unit of text is a word and a phrase is a list of words. (We defer the discussion of more complex structures till the "Methodology" section.) Associated with each phrase is a history of the frequency of occurrence of the phrase, obtained by partitioning the documents based upon their timestamps. The frequency of occurrence in a particular time period is the number o...

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