TAILIEUCHUNG - Descriptive phrase extraction in text mining
In this paper we present a framework for text mining using descriptive phrase extraction. The framework follows the general knowledge discovery process, thus containing steps from preprocessing to the utilization of the results. We apply generalized episodes and episode rules data mining method. | ISSN:2249-5789 Alekhya V et al , International Journal of Computer Science & Communication Networks,Vol 2(5), 601-606 Descriptive Phrase Extraction in Text mining Alekhya V1, B. Govinda Laxmi2 *(, Department of CSE, Sri Sivani college of engineering, Andhra Pradesh, India) ** (Associate professor, Department of CSE, Sri Sivani college of engineering, Andhra Pradesh, India Abstract Recently various algorithms have been proposed for text documents to mining frequent patterns. But how to efficiently find these patterns is still an open issue in text mining domain. Traditionally, texts have been analyzed by using various information retrieval related methods, such as full-text analysis, and natural language processing. However, only few examples of data mining in text, particularly in full text, are available. In this paper we present a framework for text mining using descriptive phrase extraction. The framework follows the general knowledge discovery process, thus containing steps from preprocessing to the utilization of the results. We apply generalized episodes and episode rules data mining method. We introduce a weighting scheme that helps in pruning out redundant or non-descriptive phrases. Several experiments have been conducted on various data sets to calculate the performance of the proposed technique. Keywords: Text Mining, Knowledge Discovery, Data Mining, Pattern Mining 1. Introduction Huge amount full-text document collections are available for end user. The user may require an overall view of the text document collection such as which topics are covered, what kind of documents exists, and so on. In some cases, user may need to search a specific piece of data in the document. On the other hand, some users may be interested in the language itself, ., in word usages or linguistic structures. Hence in recent years, knowledge discovery and data mining have attracted a great deal of attention with an imminent need for turning such data into useful .
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