Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results.

Eui-Hong Han, George Karypis, Vipin Kumar, Bamshad Mobasher: Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results. IEEE Data Eng. Bull. 21(1): 15-22(1998)

@article{DBLP:journals/debu/HanKKM98,
  author    = {Eui-Hong Han and
               George Karypis and
               Vipin Kumar and
               Bamshad Mobasher},
  title     = {Hypergraph Based Clustering in High-Dimensional Data Sets: A
               Summary of Results},
  journal   = {IEEE Data Eng. Bull.},
  volume    = {21},
  number    = {1},
  year      = {1998},
  pages     = {15-22},
  ee        = {db/journals/debu/HanKKM98.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Clustering of data in a large dimension space is of a great interest in many data mining applications. In this paper, we propose a method for clustering of data in a high dimensional space based on a hypergraph model. In this method, the relationship present in the original data in high dimensional space are mapped into a hypergraph. A hyperedge represents a relationship (affinity) among subsets of data and the weight of the hyperedge reflects the strength of this affinity. A hypergraph partitioning algorithm is used to find a partitioning of the vertices such that the corresponding data items in each partition are highly related and the weight of the hyperedges cut by the partitioning is minimized. We present results of experiments on two different data sets: S&P500 stock data for the period of 1994-1996 and protein coding data. These experiments demonstrate that our approach is applicable and effective in high dimensional datasets.

ACM SIGMOD Anthology

CDROM Version: Load the CDROM "Volume 1 Issue 2, SIGMOD '75-'92" and ...

Windows: Click the letter of your CD drive
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Mac: Click here
UNIX/LINUX: mount the CD and click on the path of your mount point:
/Anthology/smod7592 or /cdrom

DVD Version: Load ACM SIGMOD Anthology DVD 2" and ...

Windows: Click the letter of your CD drive
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Mac: Click here
UNIX/LINUX: mount the DVD and click on the path of your mount point:
/Anthology/aDVD2 or /dvd

Online Edition:

Data Engineering Bulletin March 1998: Mining of Large Datasets (Daniel Barbara, ed.)
( letter+figures , letter-figures , A4+figures , A4-figures , PDF+figures)

References

[AS94]: Rakesh Agrawal, Ramakrishnan Srikant: Fast Algorithms for Mining Association Rules in Large Databases. VLDB 1994: 487-499
[BDO95]: ...
[Ber76]: ...
[BMS97]: Sergey Brin, Rajeev Motwani, Craig Silverstein: Beyond Market Baskets: Generalizing Association Rules to Correlations. SIGMOD Conference 1997: 265-276
[CHY96]: Ming-Syan Chen, Jiawei Han, Philip S. Yu: Data Mining: An Overview from a Database Perspective. IEEE Trans. Knowl. Data Eng. 8(6): 866-883(1996)
[CS96]: ...
[Han98]: ...
[HBG+98]: ...
[HHS92]: Nomi L. Harris, Lawrence Hunter, David J. States: Mega-Classification: Discovering Motifs in Massive Datastreams. AAAI 1992: 837-842
[HKKM97a]: Eui-Hong Han, George Karypis, Vipin Kumar, Bamshad Mobasher: Clustering Based On Association Rule Hypergraphs. DMKD 1997: 0-
[HKKM97b]: ...
[Jac91]: ...
[JD88]: ...
[KAKS97]: George Karypis, Rajat Aggarwal, Vipin Kumar, Shashi Shekhar: Multilevel Hypergraph Partitioning: Application in VLSI Domain. DAC 1997: 526-529
[Kar98]: ...
[MHB+97]: ...
[NRS+95]: ...