In natural language processing (NLP), document classification is an important task that relies on the proper thematic representation of documents. Gaussian mixture-based clustering widespread for capturing rich semantics but ignores emphasizing potential terms in corpus. Moreover, soft approach causes long-tail noise by putting every word into cluster, which affects documents and their classifi...

Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use...

The paper introduces a descriptive data mining method to discover knowledge for the task of automatic categorization in document image analysis. We argue that a document image is a multi-modal unit of analysis whose semantics is deduced from a combination of textual content, layout structure and logical structure. So, the method considers simultaneously different modalities of document represen...

The main focus of this paper is document image classification and retrieval, where we analyze and compare different parameters for the RunLeght Histogram (RL) and Fisher Vector (FV) based image representations. We do an exhaustive experimental study using different document image datasets, including the MARG benchmarks, two datasets built on customer data and the images from the Patent Image Cl...