Abstract

Information Filtering is concerned with filtering data streams in such a way as to leave only pertinent data (information) to be perused. When the data streams are produced in a changing environment (as most if not all are) the filtering has to adapt too in order to remain effective. Adaptive Information Filtering (AIF) is concerned with filtering in changing environments. The changes may occur both on the transmission side (the nature of the streams can change), and on the reception side (the interest of a user can change). The thesis research described in this paper combines trigram analysis, clustering, and evolutionary computation, in an effort to create an AIF system with such useful properties as domain independence, spelling error insensitivity, adaptability, and optimal use of user feedback while minimizing the amount of user feedback required to function properly.

Department(s)

Computer Science

Comments

Master's Thesis (Leiden Univeristy, The Netherlands)

Document Type

Book

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 1996 Leiden University, All rights reserved.


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