3D Class-Preserving Projection Technique for the Representation of N-Dimensional Classified Data and Association Rules Ioannis Kopanakis (Technological Educational Institute of Crete, Heraklion Crete, Greece i.kopanakis@emark.teicrete.gr) Stefanos Karagiannis (Technological Educational Institute of Crete, Heraklion Crete, Greece skaragianis@sdo.teicrete.gr) Nikos Pelekis (Univ. of Piraeus, Piraeus, Greece npelekis@unipi.gr) Haralampos Karanikas (UMIST, Manchester, UK karanik@co.umist.ac.uk) Abstract: The visual senses for humans have a unique status, offering a very broadband channel for information flow. Visual approaches to analysis and mining attempt to take advantage of our abilities to perceive pattern and structure in visual form and to make sense of, or interpret, what we see. Visual Data Mining techniques have proven to be of high value in exploratory data analysis and they also have a high potential for mining large databases. In this work, we try to investigate and expand the area of visual data mining by proposing a new 3- Dimensional visual data mining technique for the representation and mining of classifiaction outcomes and association rules. Keywords: Visual Data Mining, Association Rules, Classification, Visual Data Mining Models Categories: I.2.4, I.2.6 1 Introduction & Motivation Classification is a primary method for machine learning and data mining [Frawley, 92]. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a pre-processing step for other algorithms operating on the detected clusters. The main enquiries that the knowledge engineer usually has on his/her attempt to understand the classification outcomes are: How well separated are the different classes? What classes are similar or dissimilar to each other? What kind of surface separates various classes, (i.e. are the classes linearly separable?) How coherent or well formed is a given class? Those questions are difficult to be answered by applying the conventional statistical methods over the raw data produced by the classification algorithm. Unless Proceedings of I-KNOW ’05 Graz, Austria, June 29 - July 1, 2005