Scalable Model for Extensional and Intensional Descriptions of Unclassified Data

Knowledge discovery from unlabeled data comprises two main tasks: identification of "natural groups" and analysis of these groups in order to interpret their meaning. These tasks are accomplished by unsupervised and supervised learning, respectively, and correspond to the taxonomy and explanation phases of the discovery process described by Langley [9]. The efforts of Knowledge Discovery from Databases (KDD) research field has addressed these two processes into two main dimensions: (1) scaling up the learning algorithms to very large databases, and (2) improving the efficiency of the knowledge discovery process. In this paper we argue that the advances achieved in scaling up supervised and unsupervised learning algorithms allow us to combine these two processes in just one model, providing extensional (who belongs to each group) and intensional (what features best describe each group) descriptions of unlabeled data. To explore this idea we present an artificial neural network (ANN) architecture, using as building blocks two well-know models: the ART1 network, from the Adaptive Resonance Theory family of ANNs [4], and the Combinatorial Neural Model (CNM), proposed by Machado ([11] and [12])). Both models satisfy one important desiderata for data mining, learning in just one pass of the database. Moreover, CNM, the intensional part of the architecture, allows one to obtain rules directly from its structure. These rules represent the insights on the groups. The architecture can be extended to other supervised/unsupervised learning algorithms that comply with the same desiderata.