Pattern and Cluster Mining on Text Data
Deepak Agnihotri, Kesari Verma, Priyanka Tripathi · 2014
Due to heavy use of electronics devices nowadays most of the information is available in electronic format and a substantial portion of information is stored as text such as in news articles, technical papers, books, digital libraries, email messages, blogs, and web pages. Mining the knowledge like pattern finding or clustering of similar kind of words is one of the important issues nowadays. This paper focuses on mining the important information from the text data. This paper uses the stories data set from project Guttenbergs William Shakespeare stories dataset for experimental study. R is used as Text Mining and statistical analysis tool in Ubuntu 12.04 LTS Linux Operating System. Frequent pattern mining is used to find the frequent terms, appeared in the documents and word Association among two or more words is measured at a given threshold value. Our algorithm uses cosine similarity in order to measure the distance between the words before clustering. The algorithm may be use to find the similarity between stories, news, emails. In this paper k-means and hierarchical agglomerative clustering algorithm is used to form the cluster.