Text clustering and classification /(Klastrování a klasifikace textů)

Text clustering and classi cation are important machine learning tasks. In this work, a combination of their approaches is presented. The main purpose was to automatically prepare a set of clusters (or generally concepts), which would subsequently serve as a training data for learning of a classiffi er. This work comprises of theoretical background, implementation details and experimental results of clustering and classi cation of text documents. A train set of documents is rst hierarchically clustered by the bisecting k-means algorithm. The result is o ered to an expert for modifi cations and possible improvements of the hierarchy. Following this, the resulting structure is used for learning of a naive Bayes classi er and a test set of documents is classi ed by it. A program was developed to perform these tasks and its results are evaluated and compared in processing document collections written in both English and Czech