Kankanala, Laxmi and Narasimha Murty, M (2007) Hybrid approaches for clustering. In: Proceedings of Pattern Recognition and Machine Intelligence (PREMI 2007), Lecture Notes in Computer Science, LNCS 4815, Springer.
Hybrid_Approaches.pdf - Published Version
Restricted to Registered users only
Download (294Kb) | Request a copy
Applications in various domains often lead to very large and frequently high-dimensional data. Successful algorithms must avoid the curse of dimensionality but at the same time should be computationally efficient. Finding useful patterns in large datasets has attracted considerable interest recently. The primary goal of the paper is to implement an efficient Hybrid Tree based clustering method based on CF-Tree and KD-Tree, and combine the clustering methods with KNN-Classification. The implementation of the algorithm involves many issues like good accuracy, less space and less time. We will evaluate the time and space efficiency, data input order sensitivity, and clustering quality through several experiments.
|Item Type:||Conference Paper|
|Additional Information:||Copyright of this article belongs to Springer.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||18 Oct 2011 04:50|
|Last Modified:||18 Oct 2011 04:50|
Actions (login required)