Augusto Villa Monte

Obtaining Classification Rules Using lvqPSO

Technological advances nowadays have made it possible for processes to handle large volumes of historic information whose manual processing would be a complex task. Data mining, one of the most significant stages in the knowledge discovery and data mining KDD process, has a set of techniques capable of modeling and summarizing these historical data, making it easier to understand them and helping the decision making process in future situations. This article presents a new data mining adaptive technique called lvqPSO that can build, from the available information, a reduced set of simple classification rules from which the most significant relations between the features recorded can be derived. These rules operate both on numeric and nominal attributes, and they are built by combining a variation of a population metaheuristic and a competitive neural network. The method proposed was compared with several methods proposed by other authors and measured over 15 databases, and satisfactory results were obtained.

Document summarization using a scoring-based representation

Currently, data repositories contain a plethora of information in different formats, most of which consists of text. This situation has raised interest in the study of techniques to automate the identification of the most relevant sentences of a document with the goal of generating a text summary. This article presents a technique for extracting the most representative sentences in a document, employing a user-defined criteria. The criteria is learned by the system using an optimization technique and a training document where the user has ranked the sentences according to their relevance. The proposed method has been applied to a five-chapter thesis with good results. At the end of this paper we provide some conclusions as well as ideas for future work.