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
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.