Title: An optimal record retrieval technique in text mining using GSO-based prefix span algorithm and improved K-means
Authors: Arumugam Rajesh Kumar; R. Sasikala; A.M. Kalpana
Addresses: Anna University, Chennai, India ' School of Computing Science and Engineering, VIT University, Vellore, India ' Department of Computer Science and Engineering, Government College of Engineering, Salem, India
Abstract: The data mining, nowadays, has surfaced as an investigative procedure devoted to the exploration of the data in the hunt for the reliable patterns or methodical associations between the variables. In the current investigation, an earnest effort made to employ an effective hybrid clustering approach to cluster the record and regain the record in accordance with the pattern mining. The novel technique consists of two vital steps such as the training and testing stages. In the training stage, the closed itemsets of each record are extorted by means of the support values, paving the way for the incredible decrease in the error making items. In the testing stage, the records having identical or approximately identical weights are clustered by means of the hybrid K-means-GSO clustering algorithm. Consequently, records at the apex of rank list are regained from the testing stage. The epoch-making technique is performed in the Java platform.
Keywords: data mining; text pattern mining; closed itemsets; normalised D-patterns; noise negative records; K-means-GSO clustering; record retrieval; k-means clustering; group search optimisation; GSO.
International Journal of Business Intelligence and Data Mining, 2016 Vol.11 No.3, pp.264 - 281
Available online: 29 Jan 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article