Scalable clustering by iterative partitioning and point attractor representation

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
Sammlungen
  • metadata
ISSN
1556-4681
Ausgabe der Veröffentlichung
1
Zeitschrift
ACM transactions on knowledge discovery from data
Schlüsselwörter
  • 004 Informatik
  • 004 Data processing
Sprache
eng
Paginierung
Art. 5
Datum der Veröffentlichung
2016
Herausgeber
ACM
Herausgeber URL
http://dx.doi.org/10.1145/2934688
Datum der Datenerfassung
2020
Datum, an dem der Datensatz öffentlich gemacht wurde
2020
Zugang
Public
Titel
Scalable clustering by iterative partitioning and point attractor representation
Ausgabe der Zeitschrift
11

Datenquelle: METADATA.UB

Andere Metadatenquellen:
Autoren
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000382878300005&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1145/2934688
eISSN
1556-472X
Externe Identifier
  • Clarivate Analytics Document Solution ID: DV4EO
ISSN
1556-4681
Ausgabe der Veröffentlichung
1
Zeitschrift
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
Schlüsselwörter
  • High-performance algorithm
  • scalable clustering
  • synchronization
Artikelnummer
ARTN 5
Datum der Veröffentlichung
2016
Status
Published
Titel
Scalable Clustering by Iterative Partitioning and Point Attractor Representation
Sub types
  • Article
Ausgabe der Zeitschrift
11

Datenquelle: Web of Science (Lite)

Abstract
<jats:p>Clustering very large datasets while preserving cluster quality remains a challenging data-mining task to date. In this paper, we propose an effective scalable clustering algorithm for large datasets that builds upon the concept of synchronization. Inherited from the powerful concept of synchronization, the proposed algorithm, CIPA (Clustering by Iterative Partitioning and Point Attractor Representations), is capable of handling very large datasets by iteratively partitioning them into thousands of subsets and clustering each subset separately. Using dynamic clustering by synchronization, each subset is then represented by a set of point attractors and outliers. Finally, CIPA identifies the cluster structure of the original dataset by clustering the newly generated dataset consisting of points attractors and outliers from all subsets. We demonstrate that our new scalable clustering approach has several attractive benefits: (a) CIPA faithfully captures the cluster structure of the original data by performing clustering on each separate data iteratively instead of using any sampling or statistical summarization technique. (b) It allows clustering very large datasets efficiently with high cluster quality. (c) CIPA is parallelizable and also suitable for distributed data. Extensive experiments demonstrate the effectiveness and efficiency of our approach.</jats:p>
Autoren
DOI
10.1145/2934688
eISSN
1556-472X
ISSN
1556-4681
Ausgabe der Veröffentlichung
1
Zeitschrift
ACM Transactions on Knowledge Discovery from Data
Sprache
en
Online publication date
2016
Paginierung
1 - 23
Datum der Veröffentlichung
2017
Status
Published
Herausgeber
Association for Computing Machinery (ACM)
Herausgeber URL
http://dx.doi.org/10.1145/2934688
Datum der Datenerfassung
2022
Titel
Scalable Clustering by Iterative Partitioning and Point Attractor Representation
Ausgabe der Zeitschrift
11

Datenquelle: Crossref

Autoren
Zeitschrift
ACM Trans. Knowl. Discov. Data
Artikelnummer
1
Paginierung
5:1 - 5:1
Datum der Veröffentlichung
2016
Titel
Scalable Clustering by Iterative Partitioning and Point Attractor Representation.
Ausgabe der Zeitschrift
11

Datenquelle: DBLP

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