Clustering high dimensional data using subspace and projected clustering algorithms

@article{Sembiring2010ClusteringHD,
  title={Clustering high dimensional data using subspace and projected clustering algorithms},
  author={Rahmat Widia Sembiring and Jasni Mohamad Zain and Abdullah Embong},
  journal={CoRR},
  year={2010},
  volume={abs/1009.0384}
}
Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerates clusters of objects in all subspaces of a dataset. It tends to produce many over lapping clusters. Approach: Subspace clustering and projected clustering are research areas for clustering in high dimensional spaces. In this research we experiment three clustering oriented algorithms, PROCLUS, P3C and STATPC. Results… CONTINUE READING
Highly Cited
This paper has 39 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 13 extracted citations

EDSC: Efficient document subspace clustering technique for high-dimensional data

2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT) • 2016
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 14 references

Subspace clustering for high dimensional categorical data

SIGKDD Explorations • 2004
View 4 Excerpts
Highly Influenced

A Novel Approach for High Dimensional Data Clustering

2010 Third International Conference on Knowledge Discovery and Data Mining • 2010
View 2 Excerpts

Constructing the search for a job in academia from the perspective of self-regulated learning strategies and social International journal of computer science & information

Wang, Chuang, Ya-yu Lo, Yaoying Xu, Yan Wang
Technology (IJCSIT) Vol.2, • 2007
View 2 Excerpts

Local Scaled Density Based Clustering, ICANNGA

Bicici, Ergun, Deniz Yuret
2007
View 2 Excerpts

P3C: A Robust Projected Clustering Algorithm

Sixth International Conference on Data Mining (ICDM'06) • 2006
View 2 Excerpts