Document image analysis such as text classification and layout analysis allow for the automated extraction of document properties. In general these methodologies are pre-processing steps for Optical Character Recognition (OCR) systems. In contrast, the proposed method aims at clustering document snippets so that an automated clustering of documents can be performed. First, localized words are c...

Clustering of short-text collections is a very relevant research area, given the current and future mode for people to use “small-language” (e.g. blogs, snippets, news and text-message generation such as email or chat). In recent years, a few approaches based on Particle Swarm Optimization (PSO) have been proposed to solve document clustering problems. However, the particularities that arise wh...

In our rst participation in TREC, our focus was on improving the basic ranking systems and applying text clustering techniques for query expansion. We tested a variety of techiniques including reference measures, passage retrieval, and data fusion for the basic ranking systems. Some techiniques were used in the o cial run, others were not used because of time limitations. We applied the text cl...

Document clustering generates clusters from the whole document collection automatically and is used in many fields, including data mining and information retrieval. Clustering text data faces a number of new challenges. Among others, the volume of text data, dimensionality, sparsity and complex semantics are the most important ones. These characteristics of text data require clustering techniqu...

-There are various clustering models introduced for unsupervised learning. PFCM or the possibilistic c-means model was proposed in 2005. PFCM produces mainly three values: the typicality values, membership values and the centres of the clusters. It is a hybrid model of PCM and FCM. We propose an extension to PFCM so that it can be used to cluster the text files. Keywords— possibilistic model, f...

Localizing text regions in images taken from natural scenes is one of the challenging problems dueto variations in font, size, color and orientation of text. In this paper, we introduce a new concept socalled Edge Color Signature for localizing text regions in an image. This method is able to localizeboth Farsi and English texts. In the proposed method rst a pyramid using diff...

Affinity propagation (AP) is a clustering method that can find data centers or clusters by sending messages between pairs of data points. Seed Affinity Propagation is a novel semisupervised text clustering algorithm which is based on AP. AP algorithm couldn’t cope up with part known data direct. Therefore, focusing on this issue a semi-supervised scheme called incremental affinity propagation c...

Clustering textual contents is an important step in mining useful information on the web or other text-based resources. The common task in text clustering is to handle text in a multi-dimensional space, and to partition documents into groups, where each group contains documents that are similar to each other. However, this strategy lacks a comprehensive view for humans in general since it canno...

Clustering is the process of grouping or aggregating of data items. Sentence clustering mainly used in variety of applications such as classify and categorization of documents, automatic summary generation, organizing the documents, etc. In text processing, sentence clustering plays a vital role this is used in text mining activities. Size of the clusters may change from one cluster to another....