Date of Award


Degree Name

Master of Science


Computer Science

First Advisor

Dr. John Kapenga

Second Advisor

Dr. Donna Kaminski

Third Advisor

Dr. Elise Kapenga

Access Setting

Masters Thesis-Open Access


Projection of a high dimensional data set into a two dimensional subspace can lead to very interesting projections. Note that P dimensional data can be projected onto P orthogonal two dimensional planes, which completely determines the original data. Two dimensional orthogonal projections can be analyzed for patterns in several ways.

In this work we will be trying to explore an unknown P dimensional data set by comparing its test projections with a set of standard patterns. A set of P dimensional data is transformed into K.K grid counts and the whole process of recognition is done using these K.K grid counts. The process of recognition consists of attempting to find a principal axis, rotating the data into standard position in P space, projecting the P dimensional data set into two dimensional test pattern images, finding scaling factors, angles of rotation and match values between the standard set of patterns and the test patterns, and finally application of a decision making heuristic to drive the procedure.