Self-Organizing Map with Real-time Updating for Big Data Analysis that  Uses Bit Value Addition of the RGB Values of the Overlapped Data Points

dc.contributor.author	Adikaram, K.K.L.B.
dc.contributor.author	Jayantha, P.A.
dc.date.accessioned	2021-07-30T03:27:36Z
dc.date.available	2021-07-30T03:27:36Z
dc.date.issued	2021-03-03
dc.identifier.citation	Adikaram, K. K. L. B. & Jayantha, P. A. (2021). Self-Organizing Map with Real-time Updating for Big Data Analysis that Uses Bit Value Addition of the RGB Values of the Overlapped Data Points. 18th Academic Sessions, University of Ruhuna, Matara, Sri Lanka. 19.
dc.identifier.issn	2362-0412
dc.identifier.uri	http://ir.lib.ruh.ac.lk/xmlui/handle/iruor/3374
dc.description.abstract	Usually, standard Self-Organizing Maps demand the user to define the number of expected clusters. Most importantly, when there is an update of the data, the data set has to be analyzed using a pre decided algorithm. Thus, it is required to have a high processing capacity to produce real-time analysis of big data. This paper presents a Self-Organizing Maps with Real-time Updating (SOMRU) which eliminates the above-mentioned drawbacks. The proposed SOMRU uses a bitmap as the plotting area.	en_US
dc.language.iso	en	en_US
dc.publisher	University of Ruhuna	en_US
dc.subject	Big data	en_US
dc.subject	Cluster identification	en_US
dc.subject	Continuous learning	en_US
dc.subject	Kohenin’s map	en_US
dc.subject	Self-organizing feature map	en_US
dc.title	Self-Organizing Map with Real-time Updating for Big Data Analysis that Uses Bit Value Addition of the RGB Values of the Overlapped Data Points	en_US
dc.type	Article	en_US