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Autonomous granulation using the Mountain Method
Author(s) -
Mazlack Lawrence J.,
Zhu Yaoyao,
He Aijing
Publication year - 2005
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20073
Subject(s) - cluster analysis , data mining , partition (number theory) , computer science , cluster (spacecraft) , context (archaeology) , granulation , artificial intelligence , mathematics , engineering , geography , geotechnical engineering , archaeology , combinatorics , programming language
We are interested in autonomous or unsupervised data mining. In the broad context of our work, we were interested in partitioning data in order to increase the information within each partition. An important aspect of this was data granulation. Clustering is an important aspect of data mining; it enables us to granulize the data. However, classic clustering methods require either the number of clusters and/or the approximate cluster centers. To avoid providing guidance, we used a variation of the Mountain Method to develop clustering parameters. This gave us satisfactory cluster centers. The estimated clusters' centers were then used in the data mining algorithm to develop and refine partitions. Experimental results of using the method on some data sets are reported. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 415–432, 2005.