Design of an Efficient Clustering using GNG and SOM
Sundeep Kumar1, Shilpi Gupta2
1Sundeep Kumar, Department of Computer Science, Mahamaya Technical University, JSS Academy of Technical Education, Noida (U.P), India.
2Shilpi Gupta, Department of Computer Science, Mahamaya Technical University, JSS Academy of Technical Education, Noida (U.P), India.
Manuscript received on 11 March 2014 | Revised Manuscript received on 20 March 2014 | Manuscript Published on 30 March 2014 | PP: 48-51 | Volume-3 Issue-10, March 2014 | Retrieval Number: J15320331014/14©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Clustering is the process of grouping the data into classes so that objects have the high similarity in comparison to one another object within a cluster. Because they are very dissimilar to object in other clusters. Dissimilarities are assessed based on the attribute value describing the object. Different types of raw data are available on the World Wide Web. Various data mining techniques can be applied on raw data to manage and organize like data preprocessing. The preprocess data is achieved through data cleaning, data reduction and data integration algorithm which can be used in variety of applications such as Clustering, Neural Network, association rules, and sequential pattern etc. In this paper we performed the data preprocessing activities like data cleaning, data reduction, data integration and related algorithm. A novel approach Growing Neural Gas and Self Organizing Maps algorithms is introduced and apply on preprocess data for clustering and performance evaluated through certain parameter error graph, time elapsed and mean weight difference kind of clustering.
Keywords: Clustering, Growing Neural Gas (GNG), SOM (Self Organizing Map), Data Preprocessing.
Scope of the Article: Clustering