International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 04 | Apr -2017
p-ISSN: 2395-0072
www.irjet.net
Effective Pattern Discovery for Text Mining Dipali C. Sonawane1, Tejal P. Shirole2, Kajal D. Patil3, Priyanka V. Patil4, , Amol K. Patil5 Student, Dept. of Computer Engineering, SSBT COET, Jalgaon, Maharashtra, India. Dept. of Computer Engineering, SSBT COET, Jalgaon, Maharashtra, India. 3Student, Dept. of Computer Engineering, SSBT COET, Jalgaon, Maharashtra, India. 4Student, Dept. of Computer Engineering , SSBT COET, Jalgaon, Maharashtra, India. 5Student, Dept. of Computer Engineering, SSBT COET, Jalgaon, Maharashtra, India. 1
2Student,
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Abstract - In text documents, Data mining techniques have
been proposed for mining useful patterns. Mining discovered pattern is still an open issue that how it is effectively use and update, In the domain of text. Term-based approaches are adopted by most existing text mining methods. By the problems of polysemy and synonymy they all suffer. An innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, Text Mining presents. using and updating discovered patterns to improve the effectiveness of for finding relevant and interesting information.
Key Words: Text mining, text classification, pattern mining.
1.INTRODUCTION Many data mining techniques have been proposed for extracting useful patterns in text documents. Text mining is the discovery of interesting knowledge in text documents. It is a demanding problem to find accurate knowledge in text documents to help users to find what they want. Data mining techniques have been used for text analysis by extracting occurring terms as descriptive phrases from document groups[1]. Many applications, such as market analysis and business management, can benefit from the use of the information and knowledge taken from a large amount of data. Knowledge discovery can be effectively used and update discovered patterns and apply it to the field of text mining. Data mining is, therefore, an important step in the process of knowledge finding in databases, which means data mining is having all methods of knowledge discovery process and performing modeling phase that is an application of methods and algorithm for calculation of search pattern or models.
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The interesting knowledge is being found by Text mining in text documents. Find correct knowledge in text documents to help users to find what they want, is a challenging issue. Many term-based methods were supplied by Information Retrieval (IR) to solve this challenge, In the beginning. Term based methods include The advantages such as efficient computational performance as well as mature theories for term weighting, which have appeared over the last couple of decades from the IR and machine learning section. Suffer term based methods from the problems of polysemy and synonymy, a word has many meanings means polysemy and many words having the same meaning is called synonymy. Many discovered terms of the semantic meaning of are uncertain for answering what users want. In the last decade many data mining methods have been proposed. In the field of text, mining is difficult and ineffective using this discovered knowledge. Because of some useful long patterns with high specificity lack in support. From data mining techniques lead to the ineffective performance was derived by misinterpretations of patterns[2]. To overcome the low-frequent and misinterpretation problems for text mining has proposed by an effective pattern discovery technique. Two processes are used by the proposed method. To refine the discovered patterns in text documents, uses pattern deploying and pattern evolving.
1.2 Motivation
In a collection is now popular such search only marginally supports discovery because the user has to determine on the words to look for, While the ability to search for keywords or phrases. Text mining results can suggest “interesting” patterns look at, On the other hand, and the user can then accept or reject these patterns as interesting. Descriptive frequent patterns are taken by pattern taxonomy model by pruning the unimportant ones is present In this research[3]. Based on their repetitions patterns are sorted.
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