ملخص الدراسة:

Text in web pages is based on expert opinion of a large number of people including the views of authors. These views are based on cultural or community aspects which make extracting information from text very difficult. Search in text usually finds text similarities between paragraphs in documents. This paper proposes a framework for data clustering and visualization called SOMvisua. SOMvisua is based on a graph representation of data input for Self-Organizing Map (SOM) and Growing Hierarchically Self-Organizing Map (GHSOM) algorithms. In SOMvisua sentences from an input article are represented as graph model instead of vector space model. SOM and GHSOM clustering algorithms construct knowledge from this article. SOMvisua provides a visual animation for eight famous graph algorithms execution with animation speed control. It also presents six types of visualization. For visualization of similarity lists, we use well-known methods that take a similarity list as input and according to the used similarity measure an adjustable number of most similar sentences are arranged in visual form. In addition, this paper presents a wide variety of text searching. We conducted experiments on the SOMvisua using a large document dataset. Then we compared the performance with that of hierarchal clustering with automated topology based SOM and GHSOM clustering to prove the superiority of SOMvisua.

توثيق المرجعي (APA)

Mikki, Mohammad A.,& Kahloot, Khalid M. (2014). SOMvisua: Data Clustering And Visualization Based on SOM And GHSOM. The Fifth International Conference on Engineering and Sustainability (ICES5), The Islamic University of Gaza. 28215