Document clustering is one of the important research issues in the field of text mining, where the documents are grouped without predefined categories or labels. High dimensionality is a major challenge in document clustering. Some of the recent algorithms address this problem by using frequent term sets for clustering. This paper proposes a new methodology for document clustering based on Asso...

Text mining, in particular the clustering is mostly used by search engines to increase the recall and precision of a search query. The content of online websites (text, blogs, chats, news, etc.) are dynamically updated, never‐ theless relevant information on the changes made are not present. Such a scenario requires a dynamic text clustering method that operates without initial knowledge on a d...

Text clustering is a very important technology in the area of text data mining. The semantic calculation method can greatly improve the computational. The aim of this paper is to improve the existing text clustering algorithms, for Chinese text and used semantic clustering method. First, in similarity calculation module of the clustering, used a staged and integrated semantic similarity algorit...

Text document clustering is one of the most widely studied data mining problems. It organizes text documents into groups such that each group has similar text documents. While grouping text documents, several issues have been observed. Accuracy and Efficiency are the main issues in text document clustering. Recently, as clustering problem can be mapped to optimization problem, evolutionary opti...

Clustering of text documents is an important technique for documents retrieval. It aims to organize documents into meaningful groups or clusters. Preprocessing text plays a main role in enhancing clustering process of Arabic documents. This research examines and compares text preprocessing techniques in Arabic document clustering. It also studies effectiveness of text preprocessing techniques: ...

Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent clusters, thereby providing a basis for intuitive and informative navigation and browsing mechanisms. Partitional clustering algorithms have been recognized to be more suitable as opposed to the hierarchical clustering schemes for processing large datasets....

Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent clusters, thereby providing a basis for intuitive and informative navigation and browsing mechanisms. Text-clustering is to divide a collection of textdocuments into different categories so that documents in the same category describe the same topic such as...

The study is conducted to propose a multi-step feature (term) selection process and in semi-supervised fashion, provide initial centers for term clusters. Then utilize the fuzzy c-means (FCM) clustering algorithm for clustering terms. Finally assign each of documents to closest associated term clusters. While most text clustering algorithms directly use documents for clustering, we propose to f...

Tibetan text clustering has potential in Tibetan information processing domain. In this paper, clustering research across Chinese and Tibetan texts is proposed to benefit Chinese and Tibetan machine translation and sentence alignment. A Tibetan and Chinese keyword table is the main way to implement the text clustering across these two languages. Improved Kmeans and improved density-based spatia...

In traditional text clustering, documents appear terms frequency without considering the semantic information of each document (i.e., vector model). The property of vector model may be incorrectly classified documents into different clusters when documents of same cluster lack the shared terms. Recently, to overcome this problem uses knowledge based approaches. However, these approaches have an...