Please use this identifier to cite or link to this item:
|Title:||Fast hierarchical clustering and its validation|
Large and high-dimensional datasets
|Source:||Dash, M., Liu, H., Scheuermann, P., Tan, K.L. (2003). Fast hierarchical clustering and its validation. Data and Knowledge Engineering 44 (1) : 109-138. ScholarBank@NUS Repository. https://doi.org/10.1016/S0169-023X(02)00138-6|
|Abstract:||Clustering is the task of grouping similar objects into clusters. A prominent and useful class of algorithm is hierarchical agglomerative clustering (HAC) which iteratively agglomerates the closest pair until all data points belong to one cluster. It outputs a dendrogram showing all N levels of agglomerations where N is the number of objects in the dataset. However, HAC methods have several drawbacks: (1) high time and memory complexities for clustering, and (2) inefficient and inaccurate cluster validation. In this paper we show that these drawbacks can be alleviated by closely studying the dendrogram. Empirical study shows that most HAC algorithms follow a trend where, except for a number of top levels of the dendrogram, all lower levels agglomerate clusters which are very small in size and close in proximity to other clusters. Methods are proposed that exploit this characteristic to reduce the time and memory complexities significantly and to make validation very efficient and accurate. Analyses and experiments show the effectiveness of the proposed method. © 2002 Elsevier Science B.V. All rights reserved.|
|Source Title:||Data and Knowledge Engineering|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 22, 2018
WEB OF SCIENCETM
checked on Jan 23, 2018
checked on Feb 19, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.