A statistics-based approach to control the quality of subclusters in incremental gravitational clustering
Resource
Pattern Recognition 38 (12): 2256-2269
Journal
Pattern Recognition
Journal Volume
38
Journal Issue
12
Pages
2256-2269
Date Issued
2005
Author(s)
Abstract
As the sizes of many contemporary databases continue to grow rapidly, incremental clustering has emerged as an essential issue for conducting data analysis on contemporary databases. An incremental clustering algorithm refers to an abstraction of the distribution of the data instances generated by the previous run of the algorithm and therefore is able to cope well with the ever-growing contemporary databases. There are two main challenges in the design of incremental clustering algorithms. The first challenge is how to reduce information loss due to the data abstraction (or summarization) operations. The second challenge is that the clustering result should not be sensitive to the order of input data. This paper presents the GRIN algorithm, an incremental hierarchical clustering algorithm for numerical datasets based on the gravity theory in physics. In the design of GRIN, a statistical test aimed at reducing information loss and distortion is employed to control formation of subclusters as well as to monitor the evolution of the dataset. Due to the statistical test-based summarization approach, GRIN is able to achieve near linear scalability and is not sensitive to input ordering. © 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Type
journal article
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