Plenary lecture IV: classification methods for bibliomining
Authors: Ioana Moisil
Venue: Proceedings of the 8th conference on Simulation, modelling and optimization (2008)

Advances in information technology are having an important impact on library systems. Large collections of heterogeneous data, from ancient manuscripts to sounds, videos and spatial data are now available in electronic format. Digital libraries are capturing human knowledge and distributing it over the web. The increasing volume of data in today digital repositories and library data warehouses has determined a wide use of computer-based sophisticated analysis techniques. Special operation of data mining can be performed in order to answer questions of librarians and researchers in information science. In 2003, S. Nicholson and J. Stanton introduced a new term -- bibliomining - for data mining library systems. Therefore bibliomining is a large umbrella incorporating all data mining methods based on mathematics, statistics, operational research, machine learning, evolutionary computing, visualization techniques, and including traditional methods of analysing groups of bibliographic references as authorship, publications, and literature, specific to bibliometrics. Librarians and researchers in information science are mining library data warehouses and other library data collections in order to discover patterns and to understand library users' behaviour, their information and services needs, but also in order to evaluate and predict the effectiveness of library services, to discover trends in queries and to identify hot topics. Classification of items based on their characteristics (features, attributes, properties) in pre-defined categories is one of the most important bibliomining tasks. Classification is defined as the ordering of items in pre-defined groups (categories) or classes, based on their similarity. The classification process consists in assigning one of k labels (or classes) to each of n items derived from a specific problem. Classification predicts categorical labels. Analysis goal is to find a classification, a model or profile for each class that optimizes a combinatorial function consisting of assignment costs, based on the individual choice of label made for each item, and separation costs - based on the pair of choices made for two related items. In machine learning classification is defined as supervised learning. Classification, as a bibliomining technique, can be used for finding hidden patterns in data by deciding to what pre-defined class to assign a record of the data set, and also in prediction, to predict group membership for data instances. This lecture describes the most important classification methods (traditional approaches as classification trees, discriminant analysis, generalized linear models, modern statistical machine learning algorithms, support vector machine, belief networks, Gaussian processes, neural network, evolutionary algorithms, swarm intelligence, boosting and ensemble) and their use in mining library data collections. Research questions regarding pre-processing operations, attribute relevance and classifiers' performance will also be discussed with emphasis on the specificity of the library items to be classified.