Many applications such as news group filtering, text crawling, and document organization require real time clustering and segmentation of text data records. The categorical data stream clustering problem also has a number of applications to the problems of customer segmentation and real time trend analysis. We will present an online approach for clustering massive text and categorical data stre...

Text clustering is a critical step in text data analysis and has been extensively studied by the mining community. Most existing algorithms are based on bag-of-words model, which faces high-dimensional sparsity problems ignores structural sequence information. Deep learning-based models such as convolutional neural networks recurrent regard texts sequences but lack supervised signals explainabl...

Textual data plays an important role in the modern world. The possibilities of applying data mining techniques to uncover hidden information present in large volumes of text collections is immense. The Growing Self Organizing Map (GSOM) is a highly successful member of the Self Organising Map family and has been used as a clustering and visualisation tool across wide range of disciplines to dis...

In this study, a multistage modular self-organizing map (SOM) model is proposed for parallel web text clustering. In the first stage, the large textual datasets are divided into some small disjoint datasets (i.e., task decomposition). In the second stage, each small data set is input into different unitary SOM models for word clustering map (i.e., modularization learning). In this stage, differ...

Text categorization is continuing to be one of the most researched NLP problems due to the ever-increasing amounts of electronic documents and digital libraries. In this paper, we present a novel text categorization method that combines the Multitype Features Coselection for Clustering and a learning logic technique, called Lsquare, for constructing text classifiers. The high dimensionality of ...

The aim of this paper is to use data mining technique and opinion mining(OM) concepts to the field of health informatics. The decision making in health informatics involves number of opinions given by the group of medical experts for specific disease in the form of decision based opinions which will be presented in medical database in the form of text. These decision based opinions are then min...

Text clustering is a technique used to gather the documents which have similar content. The main objective of text clustering is to divide the unstructured set of objects into clusters. The algorithm can be used to represent the concept and to measure the similarity among the concept present in the document. The clustering process is widely applied for summarizing the corpus and document classi...

Most text mining tasks, including clustering and topic detection, are based on statistical methods that treat text as bags of words. Semantics in the text is largely ignored in the mining process, and mining results often have low interpretability. One particular challenge faced by such approaches lies in short text understanding, as short texts lack enough content from which statistical conclu...