期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2018
卷号：6
期号：2
页码：1205
DOI：10.15680/IJIRCCE.2018.0602100
出版社：S&S Publications
摘要：Knowledge discovery and data mining have attracted a great deal of attention with an imminent needfor turning such data into useful information .Knowledge discovery can be viewed as the process of non trivialextraction of information from large databases, previously unknown and potentially useful for users. Even manytechniques are available in data mining for the retrieval record based on text pattern mining; still updating discoveredpatterns is a trouble in data mining [1]. In order to tackle the problems a record retrieval method using clustering basedon text pattern mining is proposed with four stages and with two main phases, which are training and testing phases. Inthe training phase, closed item set from each record is extracted using support values, followed by the identification ofnormalized D-patterns of records. Noise negative records are also used to reduce the errors in updated item sets. Aftergetting accurate updated records, the weight for every record are computed to move for further processes [2]. And thenin testing phase, the records are clustered using Fuzzy C -Means clustering algorithm. The performance of the FCMalgorithm depends on the selection of the initial cluster center and or the initial membership value. If a good initialcluster center that is close to the actual final cluster center can be found, the FCM algorithm will converge very quicklyand the processing time can be drastically reduced. Our proposed work is to implemented in java platform over realworld datasets. The FCM algorithm is robust and able to tolerate the noisy situations that often happen in realapplication systems.
