LNAI 3171 An Efficient Clustering Method for
High Dimensional Data Mining 1st Edition by Jae
Woo Chang, Yong Ki Kim ISBN 9783540206460
354020646X pdf download
https://ebookball.com/product/lnai-3171-an-efficient-clustering-
method-for-high-dimensional-data-mining-1st-edition-by-jae-woo-
chang-yong-ki-kim-isbn-9783540206460-354020646x-8848/
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LNAI 2903 Robustness for Evaluating Rule Generalization Capability in
Data Mining 1st Edition by Dianhui Wang, Tharam Dillon, Xiaohang Ma
ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-robustness-for-
evaluating-rule-generalization-capability-in-data-mining-1st-
edition-by-dianhui-wang-tharam-dillon-xiaohang-ma-
isbn-9783540206460-354020646x-11134/
LNAI 3191 Multi agent Systems and Distributed Data Mining 1st Edition
by Chris Giannella, Ruchita Bhargava, Hillol Kargupta ISBN
9783540206460 354020646X
https://ebookball.com/product/lnai-3191-multi-agent-systems-and-
distributed-data-mining-1st-edition-by-chris-giannella-ruchita-
bhargava-hillol-kargupta-isbn-9783540206460-354020646x-11934/
LNAI 3127 Concept Based Data Mining with Scaled Labeled Graphs 1st
Edition by Bernhard Ganter, Peter Grigoriev, Sergei Kuznetsov, Mikhail
Samokhin ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-3127-concept-based-data-
mining-with-scaled-labeled-graphs-1st-edition-by-bernhard-ganter-
peter-grigoriev-sergei-kuznetsov-mikhail-samokhin-
isbn-9783540206460-354020646x-13116/
LNAI 2903 Evaluating a Nearest Neighbor Method to Substitute
Continuous Missing Values 1st Edition by Eduardo Hruschka, Estevam
Hruschka Jr, Nelso Ebecken ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-evaluating-a-nearest-
neighbor-method-to-substitute-continuous-missing-values-1st-
edition-by-eduardo-hruschka-estevam-hruschka-jr-nelso-ebecken-
isbn-9783540206460-354020646x-10846/
,LNAI 2903 Pareto Neuro Ensembles 1st Edition by Hussein Abbass ISBN
9783540206460 354020646X
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ensembles-1st-edition-by-hussein-abbass-
isbn-9783540206460-354020646x-9200/
LNAI 2903 Representing the Spatial Relations in the Semantic Web
Ontologies 1st Edition by Hyunjang Kong, Kwanho Jung, Junho Choi,
Wonpil Kim, Pankoo Kim, Jongan Par ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-representing-the-spatial-
relations-in-the-semantic-web-ontologies-1st-edition-by-hyunjang-
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isbn-9783540206460-354020646x-9178/
LNAI 2903 Model Based Reinforcement Learning for Alternating Markov
Games 1st Edition by Drew Mellor ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-model-based-
reinforcement-learning-for-alternating-markov-games-1st-edition-
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Clustering for Data Mining A Data Recovery Approach 1st Edition by
Boris Mirkin ISBN 1584885343 978-1584885344
https://ebookball.com/product/clustering-for-data-mining-a-data-
recovery-approach-1st-edition-by-boris-mirkin-
isbn-1584885343-978-1584885344-12828/
An Efficient Multidimensional Data Model for Web Usage Mining 1st
edition by Edmond Wu, Michael Ng, Joshua Huang ISBN 3540213710
9783540213710
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, An Efficient Clustering Method
for High-Dimensional Data Mining
Jae-Woo Chang and Yong-Ki Kim
Dept. of Computer Engineering
Research Center for Advanced LBS Technology
Chonbuk National University, Chonju, Chonbuk 561-756, South Korea
{jwchang,ykkim}@dblab.chonbuk.ac.kr
Abstract. Most clustering methods for data mining applications do not work ef-
ficiently when dealing with large, high-dimensional data. This is caused by so-
called ‘curse of dimensionality’ and the limitation of available memory. In this
paper, we propose an efficient clustering method for handling of large amounts
of high-dimensional data. Our clustering method provides both an efficient cell
creation and a cell insertion algorithm. To achieve good retrieval performance
on clusters, we also propose a filtering-based index structure using an approxi-
mation technique. We compare the performance of our clustering method with
the CLIQUE method. The experimental results show that our clustering method
achieves better performance on cluster construction time and retrieval time.
1 Introduction
Data mining is concerned with extraction of information of interest from large
amounts of data, i.e. rules, regularities, patterns, constraints. Data mining is a data
analysis technique that has been developed from other research areas such as Ma-
chine Learning, Statistics, and Artificial Intelligent. However, data mining has three
differences from the conventional analysis techniques. First, while the existing tech-
niques are mostly applied to a static dataset, data mining is applied to a dynamic data-
set with continuous insertions and deletions. Next, the existing techniques manage
only errorless data, but data mining can manage data containing some errors. Finally,
unlike the conventional techniques, data mining generally deals with large amounts of
data.
The typical research topics in data mining are classification, clustering, association
rule, and trend analysis, etc. Among them, one of the most important topics is cluster-
ing. The conventional clustering methods have a critical drawback that they are not
suitable for handling large data sets containing millions of data units because the data
set is restricted to be resident in a main memory. They do not work well for clustering
high-dimensional data because their retrieval performance is generally degraded as
the number of dimensions increases. In this paper, we propose an efficient clustering
method for dealing with a large amount of high-dimensional data. Our clustering
method provides an efficient cell creation algorithm, which makes cells by splitting
each dimension into a set of partitions using a split index. It also provides a cell inser-
A.L.C. Bazzan and S. Labidi (Eds.): SBIA 2004, LNAI 3171, pp. 276–285, 2004.
© Springer-Verlag Berlin Heidelberg 2004
High Dimensional Data Mining 1st Edition by Jae
Woo Chang, Yong Ki Kim ISBN 9783540206460
354020646X pdf download
https://ebookball.com/product/lnai-3171-an-efficient-clustering-
method-for-high-dimensional-data-mining-1st-edition-by-jae-woo-
chang-yong-ki-kim-isbn-9783540206460-354020646x-8848/
Explore and download more ebooks or textbooks
at ebookball.com
, Get Your Digital Files Instantly: PDF, ePub, MOBI and More
Quick Digital Downloads: PDF, ePub, MOBI and Other Formats
LNAI 2903 Robustness for Evaluating Rule Generalization Capability in
Data Mining 1st Edition by Dianhui Wang, Tharam Dillon, Xiaohang Ma
ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-robustness-for-
evaluating-rule-generalization-capability-in-data-mining-1st-
edition-by-dianhui-wang-tharam-dillon-xiaohang-ma-
isbn-9783540206460-354020646x-11134/
LNAI 3191 Multi agent Systems and Distributed Data Mining 1st Edition
by Chris Giannella, Ruchita Bhargava, Hillol Kargupta ISBN
9783540206460 354020646X
https://ebookball.com/product/lnai-3191-multi-agent-systems-and-
distributed-data-mining-1st-edition-by-chris-giannella-ruchita-
bhargava-hillol-kargupta-isbn-9783540206460-354020646x-11934/
LNAI 3127 Concept Based Data Mining with Scaled Labeled Graphs 1st
Edition by Bernhard Ganter, Peter Grigoriev, Sergei Kuznetsov, Mikhail
Samokhin ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-3127-concept-based-data-
mining-with-scaled-labeled-graphs-1st-edition-by-bernhard-ganter-
peter-grigoriev-sergei-kuznetsov-mikhail-samokhin-
isbn-9783540206460-354020646x-13116/
LNAI 2903 Evaluating a Nearest Neighbor Method to Substitute
Continuous Missing Values 1st Edition by Eduardo Hruschka, Estevam
Hruschka Jr, Nelso Ebecken ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-evaluating-a-nearest-
neighbor-method-to-substitute-continuous-missing-values-1st-
edition-by-eduardo-hruschka-estevam-hruschka-jr-nelso-ebecken-
isbn-9783540206460-354020646x-10846/
,LNAI 2903 Pareto Neuro Ensembles 1st Edition by Hussein Abbass ISBN
9783540206460 354020646X
https://ebookball.com/product/lnai-2903-pareto-neuro-
ensembles-1st-edition-by-hussein-abbass-
isbn-9783540206460-354020646x-9200/
LNAI 2903 Representing the Spatial Relations in the Semantic Web
Ontologies 1st Edition by Hyunjang Kong, Kwanho Jung, Junho Choi,
Wonpil Kim, Pankoo Kim, Jongan Par ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-representing-the-spatial-
relations-in-the-semantic-web-ontologies-1st-edition-by-hyunjang-
kong-kwanho-jung-junho-choi-wonpil-kim-pankoo-kim-jongan-par-
isbn-9783540206460-354020646x-9178/
LNAI 2903 Model Based Reinforcement Learning for Alternating Markov
Games 1st Edition by Drew Mellor ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-model-based-
reinforcement-learning-for-alternating-markov-games-1st-edition-
by-drew-mellor-isbn-9783540206460-354020646x-10942/
Clustering for Data Mining A Data Recovery Approach 1st Edition by
Boris Mirkin ISBN 1584885343 978-1584885344
https://ebookball.com/product/clustering-for-data-mining-a-data-
recovery-approach-1st-edition-by-boris-mirkin-
isbn-1584885343-978-1584885344-12828/
An Efficient Multidimensional Data Model for Web Usage Mining 1st
edition by Edmond Wu, Michael Ng, Joshua Huang ISBN 3540213710
9783540213710
https://ebookball.com/product/an-efficient-multidimensional-data-
model-for-web-usage-mining-1st-edition-by-edmond-wu-michael-ng-
joshua-huang-isbn-3540213710-9783540213710-10862/
, An Efficient Clustering Method
for High-Dimensional Data Mining
Jae-Woo Chang and Yong-Ki Kim
Dept. of Computer Engineering
Research Center for Advanced LBS Technology
Chonbuk National University, Chonju, Chonbuk 561-756, South Korea
{jwchang,ykkim}@dblab.chonbuk.ac.kr
Abstract. Most clustering methods for data mining applications do not work ef-
ficiently when dealing with large, high-dimensional data. This is caused by so-
called ‘curse of dimensionality’ and the limitation of available memory. In this
paper, we propose an efficient clustering method for handling of large amounts
of high-dimensional data. Our clustering method provides both an efficient cell
creation and a cell insertion algorithm. To achieve good retrieval performance
on clusters, we also propose a filtering-based index structure using an approxi-
mation technique. We compare the performance of our clustering method with
the CLIQUE method. The experimental results show that our clustering method
achieves better performance on cluster construction time and retrieval time.
1 Introduction
Data mining is concerned with extraction of information of interest from large
amounts of data, i.e. rules, regularities, patterns, constraints. Data mining is a data
analysis technique that has been developed from other research areas such as Ma-
chine Learning, Statistics, and Artificial Intelligent. However, data mining has three
differences from the conventional analysis techniques. First, while the existing tech-
niques are mostly applied to a static dataset, data mining is applied to a dynamic data-
set with continuous insertions and deletions. Next, the existing techniques manage
only errorless data, but data mining can manage data containing some errors. Finally,
unlike the conventional techniques, data mining generally deals with large amounts of
data.
The typical research topics in data mining are classification, clustering, association
rule, and trend analysis, etc. Among them, one of the most important topics is cluster-
ing. The conventional clustering methods have a critical drawback that they are not
suitable for handling large data sets containing millions of data units because the data
set is restricted to be resident in a main memory. They do not work well for clustering
high-dimensional data because their retrieval performance is generally degraded as
the number of dimensions increases. In this paper, we propose an efficient clustering
method for dealing with a large amount of high-dimensional data. Our clustering
method provides an efficient cell creation algorithm, which makes cells by splitting
each dimension into a set of partitions using a split index. It also provides a cell inser-
A.L.C. Bazzan and S. Labidi (Eds.): SBIA 2004, LNAI 3171, pp. 276–285, 2004.
© Springer-Verlag Berlin Heidelberg 2004
