Due to the flourish of World Wide Web and the rapid development of the Internet technology, the increasing volume of digital textual data become more and more unmanageable, therefore the importance of text classification has gained significant attention. Text classification pose some specific challenges such as high dimensionality with each document (data point) having only a very small subset ...

A classic document clustering technique may incorrectly classify documents into different clusters when documents that should belong to the same cluster do not have any shared terms. Recently, to overcome this problem, internal and external knowledge-based approaches have been used for text document clustering. However, the clustering results of these approaches are influenced by the inherent s...

Vector-space and distributional methods for text document clustering are discussed. Discriminative clustering, a recently proposed method, uses external data to find taskrelevant characteristics of the documents, yet the clustering is defined even with no external data. We introduce a distributional version of discriminative clustering that represents text documents as probability distributions...

We are propose a hybrid clustering method, the methodology combines the strengths of both partitioning and agglomerative clustering methods. Clustering algorithms that build meaningful hierarchies out of large document collections are ideal tools for their interactive visualization and exploration as they provide data-views that are consistent, predictable, and at different levels of granularit...

Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw...

Most studies of data mining have focus on structured data such as relational, transactional, and data warehouse data. However, the most available information is stored in text database, which consist of large amounts of text documents such as news articles, research papers, and e-mail messages. Data stored in most text databases are unstructured data, such as abstract and contents. The ability ...

Text Clustering is a text mining technique which is used to group similar documents into single cluster by using some sort of similarity measure & separating the dissimilar documents. Popular clustering algorithms available for text clustering treats document as conglomeration of words. The syntactic or semantic relations between words are not given any consideration. Many different algorithms ...

Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this paper, we first give empirical evidence that feature selection methods can improve the efficiency and performance of text clustering algorithm. Then we propose a new feature selection method called “Term Contribution ...

Text classification is a challenging task due to the large dimensionality of the feature vector. To alleviate this problem, feature reduction techniques are applied for reducing the amount of time and complexity for text classification. In this paper, we propose a novel fuzzy self constructing algorithm for feature clustering. Feature clustering is a feature reduction method which drastically r...