Entity clustering is a vital feature needed by any automatic content conversion system. Such a system constructs a digital document from a hard copy of a newspaper, book, etc. At application level, the system will process an image (typically black and white) and identify the various content layout elements, such as paragraphs, tables, images, columns, etc. Here is where the entity clustering me...

Keeping of old databases is difficult. Especially when the documentaries of system are infirm or written documentaries are deleted. Reverse engineering methods are tried to improve and solving this problem. Various methods and conceptive models are presented for discovering and extraction old databases. Some of these methods are tried to do normalization up to different levels. But after normal...

Document categorization is a daily task in every organization, but it is a very subjective process. While automatic document categorization has been widely studied, much challenging research still remains to support user subjective categorization. This study evaluates and compares the application of Self-Organizing Maps (SOM) and Learning Vector Quantization (LVQ) to automatic document classifi...

In this paper a novel solution to automatic and unsupervised word sense induction (WSI) is introduced. It represents an instantiation of the ‘one sense per collocation’ observation (Gale et al., 1992). Like most existing approaches it utilizes clustering of word co-occurrences. This approach differs from other approaches to WSI in that it enhances the effect of the one sense per collocation obs...

As electronic commerce and knowledge economy environments proliferate, both individuals and organizations increasingly generate and consume large amounts of online information, typically available as textual documents. To manage this ever-increasing volume of documents, such individuals and organizations frequently organize their documents into categories that facilitate document management and...

A method for automatic liver tumor segmentation from computer tomography (CT) images is presented in this paper. Segmentation is an important operation before surgery planning, and automatic methods offer an alternative to laborious manual segmentation. In addition, segmentations of automatic methods are reproducible, so they can be reliably evaluated and they do not depend on the performer of ...

This paper proposes an automatic segmentation algorithm that combines clustering and deformable models. First, a k-means clustering is performed based on the image intensity. A hierarchical recognition scheme is then used to recognize the structure to be segmented, and an initial seed is constructed from the recognized region. The seed is then evolved under certain deformable model mechanism. T...

Term recognition and clustering are key topics in automatic knowledge acquisition and text mining. In this paper we present a novel approach to the automatic discovery of term similarities, which serves as a basis for both classification and clustering of domain-specific concepts represented by terms. The method is based on automatic extraction of significant patterns in which terms tend to app...

Pruning spiking networks : effects of sparsity on phase-coding and storage capacity A quality-driven ensemble approach to automatic model selection in clustering

In this paper the new method for automatic segmentation of cup region from fundus eye images taken from classical fundus camera. The proposed method which is based on fuzzy clustering algorithm is a first step in automatic classification of fundus eye images into normal and glaucomatous ones.