Main Article Content
Today, we are witnessing enormous growth in data volume. Often, data is distributed or it can be in the form of streaming data.
Efficient clustering in this entire scenario becomes a very challenging problem. Our work is in the context of K-means clustering algorithm. Kmeans
clustering has been one of the popular clustering algorithms. It requires several passes on the entire dataset, which can make it very
expensive for large disk-resident datasets and also for streaming data. In view of this, a lot of work has been done on various approximate
versions of k-means, which require only one or a small number of passes on the entire dataset. In our work has developed a new algorithm for
very large data clustering which typically requires only one or a small number of passes on the entire dataset. The algorithm uses sampling to
create initial cluster centers, and then takes one or more passes over the entire dataset to adjust these cluster centers. We have implemented to
develop clustering algorithm for distributed data set. The main contribution of this paper is the implementation and evaluation of that algorithm.
Our experiments show that this framework can be very effective in clustering evolving streaming data.
Key words- Data Mining; Clustering; Distributed k-means Algorithm; Search Engine Technique.
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.