NDLI: Integrating K-means clustering with a relational DBMS using SQL

Content Provider	IEEE Xplore Digital Library
Author	Ordonez, C.
Copyright Year	1989
Abstract	Integrating data mining algorithms with a relational DBMS is an important problem for database programmers. We introduce three SQL implementations of the popular K-means clustering algorithm to integrate it with a relational DBMS: 1) a straightforward translation of K-means computations into SQL, 2) an optimized version based on improved data organization, efficient indexing, sufficient statistics, and rewritten queries, and 3) an incremental version that uses the optimized version as a building block with fast convergence and automated reseeding. We experimentally show the proposed K-means implementations work correctly and can cluster large data sets. We identify which K-means computations are more critical for performance. The optimized and incremental K-means implementations exhibit linear scalability. We compare K-means implementations in SQL and C++ with respect to speed and scalability and we also study the time to export data sets outside of the DBMS. Experiments show that SQL overhead is significant for small data sets, but relatively low for large data sets, whereas export times become a bottleneck for C++.
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	188
Ending Page	201
Page Count	14
File Size	804634
File Format	PDF
ISSN	10414347
Volume Number	18
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Relational databases Data mining Programming profession Partitioning algorithms Scalability Computer languages Indexing Statistics Convergence relational DBMS. Index Terms- Clustering K-means SQL
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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