Adaptive Information Filtering as a means to overcome Information Overload

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.