Date of Award


Publication Type

Master Thesis

Degree Name



Computer Science

First Advisor

Frost, R.


Computer Science.




In artificial intelligence there is a great need to represent temporal knowledge and to reason about models that capture change over time. Change seems to be constant in a continuously changing world. In many domains such as science, medicine, finance, and demographics, change is noticeable from one time to another. The thesis work aims at first extending FCA to capture temporal evolutions (TFCA) represented by concept lattices in time-stamped databases, and at applying the extended FCA techniques to data mining with an endeavor of inferring temporal properties. Extending formal concept analysis to temporal domains allows us to use concept lattices to visualize temporal evolutions and deduce insights on the hidden regularities in the data. To represent temporal evolutions, formal entities are time indexed. Temporal edges are added to concept lattices to show evolutions. Important temporal properties such as class evolution, persistence, and transition are classified and a mechanism for inferring them is presented. Algorithms for inferring temporal properties and generating temporal lattices from time-stamped databases are developed, implemented, and tested.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .N46. Source: Masters Abstracts International, Volume: 40-06, page: 1551. Advisers: Richard A. Frost; Ahmed Y. Tawfik. Thesis (M.Sc.)--University of Windsor (Canada), 2001.