An efficient method for batch updates in OPTICS cluster ordering
by Dhruv Kumar; Poonam Goyal; Navneet Goyal
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 10, No. 1, 2018

Abstract: DBSCAN is one of the popular density-based clustering algorithms, but requires re-clustering the entire data when the input parameters are changed. OPTICS overcomes this limitation. In this paper, we propose a batch-wise incremental OPTICS algorithm which performs efficient insertion and deletion of a batch of points in a hierarchical cluster ordering, which is the output of OPTICS. Only a couple of algorithms are available in the literature on incremental versions of OPTICS. This can be attributed to the sequential access patterns of OPTICS. The existing incremental algorithms address the problem of incrementally updating the hierarchical cluster ordering for point-wise insertion/deletion, but these algorithms are only good for infrequent updates. The proposed incremental OPTICS algorithm performs batch-wise insertions/deletions and is suitable for frequent updates. It produces exactly the same hierarchical cluster ordering as that of classical OPTICS. Real datasets have been used for experimental evaluation of the proposed algorithm and results show remarkable performance improvement over the classical and other existing incremental OPTICS algorithms.

Online publication date: Sun, 25-Mar-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com