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New Optimization Algorithm for Data Clustering
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논문 기본 정보

Type
Academic journal
Author
Journal
Korea Intelligent Information Systems Society Journal of Intelligence and Information Systems Vol. 13 No.3 KCI Accredited Journals
Published
2007.9
Pages
31 - 45 (15page)

Usage

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Topic
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Background
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Method
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Result
New Optimization Algorithm for Data Clustering
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Abstract· Keywords

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Large data handling is one of critical issues that the data mining community faces. This is particularly true for computationally intense tasks such as data clustering. Random sampling of instances is one possible means of achieving large data handling, but a pervasive problem with this approach is how to deal with the noise in the evaluation of the learning algorithm. This paper develops a new optimization based clustering approach using an algorithm specifically designed for noisy performance. Numerical results show this algorithm better than the other algorithms such as PAM and CLARA. Also with this algorithm substantial benefits can be achieved in terms of computational time without sacrificing solution quality using partial data.
#Data Clustering #Optimization-Based Clustering #NP Method #Partitioning #Random Sampling

Contents

1. Introduction
2. Scalable Clustering
3. Optimization-Based Clustering
4. Numerical Results of Instance subset
5. Conclusions and Future Research
References
요약

References (28)

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1. [학술지(정기간행물)] - / 1998 / Perspectives on operations research in data and knowledge management 111 : 1 ~ 14 google schola 2. [학술지(정기간행물)] - / 1998 / UCI Repository of Machine Learning Databases google schola 3. [학술지(정기간행물)] - / 1998 / Scaling clustering algorithms to large databases : 9 ~ 15 google schola 4. [학술지(정기간행물)] - / 2002 / Scaling mining algorithms to large databases 45 (8) : 38 ~ 43 google schola 5. [학술지(정기간행물)] - / 1998 / Feature selection via mathematical programming 10 : 209 ~ 217 google schola

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