Next Generation Online Trading
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Date
2013
Authors
Maduranga, M.A.M.
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Journal ISSN
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Publisher
Uva Wellassa University of Sri Lanka
Abstract
Identifying segments of customers and their behavioral patterns over different time intervals, is an important application for businesses, In study with the 'electronic business to customer relationship' is very imported in today. Because purchasing through online is rapidly developing today. This is particularly important in dynamic and ever-changing markets, where customers are driven by ever changing market competition and demands.
"Next generation online trading" is a system that support for multi production organization. Mainly customers can buy product online through site. And also web application is supported for Company management level for maintenance their stocks ,productions and decision making process for the management.
The purpose of this thesis is to study, implement and analyze Data-mining technology and techniques and then do an analysis of the sample / raw data to obtain a meaningful interpretation. The data mining algorithms I have used here is a k-means clustering algorithm. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.
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Keywords
Computer Science and Technology