CFP last date
20 May 2024
Reseach Article

Mining on K-way Means Clustered Streaming Data

by J. Gitanjali, P. Dinesh Kumar, B. Suresh Babu, S. B. Prabakaran
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 84 - Number 3
Year of Publication: 2013
Authors: J. Gitanjali, P. Dinesh Kumar, B. Suresh Babu, S. B. Prabakaran
10.5120/14558-2658

J. Gitanjali, P. Dinesh Kumar, B. Suresh Babu, S. B. Prabakaran . Mining on K-way Means Clustered Streaming Data. International Journal of Computer Applications. 84, 3 ( December 2013), 32-39. DOI=10.5120/14558-2658

@article{ 10.5120/14558-2658,
author = { J. Gitanjali, P. Dinesh Kumar, B. Suresh Babu, S. B. Prabakaran },
title = { Mining on K-way Means Clustered Streaming Data },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 84 },
number = { 3 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 32-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume84/number3/14558-2658/ },
doi = { 10.5120/14558-2658 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:00:00.073784+05:30
%A J. Gitanjali
%A P. Dinesh Kumar
%A B. Suresh Babu
%A S. B. Prabakaran
%T Mining on K-way Means Clustered Streaming Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 84
%N 3
%P 32-39
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is the process of finding patterns or correlations from a different data set and changing it into the useful information. Clustering is dividing the data into groups that are similar in behavior among the data sets in a group and distinct across the groups. Data Stream mining is very important and challenging problem, because in business transactions we need to make better managerial choices and extract the essence of this streaming data where the data streams are temporally ordered, fast changing, large and continuous concurrent flow of data. Our objective in this paper is to propose a model using data mining, with the help key performance indicators (variables) found for each customer, clustering will be done using K-means clustering technique on real time basis with streaming data.

References
  1. M, Kamber and J, Han. (2006). Data Mining: Concepts and Techniques, vol. 54, pp 212-225. Second edition.
  2. L. Callaghan. Et. all. 2001 "Streaming-Data Algorithms for High-Quality Clustering," Proceedings of IEEE International Conference on Data Engineering.
  3. T. Zhang. Et. all (2006) "Birch: an efficient data clustering method for very large databases," ACM SIGMOD international conference on Management of data, New York, NY, USA, pp. 103–114.
  4. Y. Chen and L. Tu, 2007. "Density-based clustering for real-time stream data," in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, ser. KDD '07, New York, NY, USA, pp. 133–142.
  5. A. Moga, I. Botan, and N. Tatbul. 2011 Upstream: Storage-centric Load Management for Streaming Applications with Update Semantics. VLDB Journal.
  6. B. Babcock. Et. all. 2002 Models and issues in data stream systems. In PODS, pages 1–16.
  7. Li Hengjie, Yang Dingxin, "Study on Data Mining and Its Application in E-business," Journal of Gansu Lianhe University (Natural Science), April 2006, pp 30-33.
  8. Young Sung Cho. Et. al. Implementation of personalized recommendation system using k-means clustering of item category based on RFM. KSCI, 13th-2 Vol, pp 1-5, Mar, 2008
  9. Lu Chuiwei, "Research and Application of Data Mining in E- commerc,"Market Modernization, April 2006, p 87.
  10. Chongsheng, Zhang. 2012. Modeling and Clustering Users with Evolving Pro?les in Usage Streams. Temporal Representation and Reasoning. Pp 66-75.
  11. Yogita. 2011. Clustering Techniques for Streaming Data. Indian Institute of Technology, Roorkee.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Clustering Data Stream Mining K-way Means Key Performance Indicators RFM.