TY - JOUR
T1 - Fastmap projection for high-dimensional data
T2 - A cluster ensemble approach
AU - Khan, Imran
AU - Ivanov, Kamen
AU - Jiang, Qingshan
N1 - Publisher Copyright:
© 2016 SERSC.
PY - 2016
Y1 - 2016
N2 - High-dimensional data with many features present a significant challenge to current clustering algorithms. Sparsity, noise, and correlation of features are common properties of high-dimensional data. Another essential aspect is that clusters in such data often exist in various subspaces. Ensemble clustering is emerging as a leading technique for improving robustness, stability, and accuracy of high-dimensional data clusterings. In this paper, we propose FastMap projection for generating subspace component data sets from high-dimensional data. By using component data sets, we create component clusterings and provides a new objective function that ensembles them by maximizing the average similarity between component clusterings and final clustering. Compared with the random sampling and random projection methods, the component clusterings by FastMap projection showed high average clustering accuracy without sacrificing clustering diversity in synthetic data analysis. We conducted a series of experiments on real-world data sets from microarray, text, and image domains employing three subspace component data generation methods, three consensus functions, and a proposed objective function for ensemble clustering. The experiment results consistently demonstrated that the FastMap projection method with the proposed objection function provided the best ensemble clustering results for all data sets.
AB - High-dimensional data with many features present a significant challenge to current clustering algorithms. Sparsity, noise, and correlation of features are common properties of high-dimensional data. Another essential aspect is that clusters in such data often exist in various subspaces. Ensemble clustering is emerging as a leading technique for improving robustness, stability, and accuracy of high-dimensional data clusterings. In this paper, we propose FastMap projection for generating subspace component data sets from high-dimensional data. By using component data sets, we create component clusterings and provides a new objective function that ensembles them by maximizing the average similarity between component clusterings and final clustering. Compared with the random sampling and random projection methods, the component clusterings by FastMap projection showed high average clustering accuracy without sacrificing clustering diversity in synthetic data analysis. We conducted a series of experiments on real-world data sets from microarray, text, and image domains employing three subspace component data generation methods, three consensus functions, and a proposed objective function for ensemble clustering. The experiment results consistently demonstrated that the FastMap projection method with the proposed objection function provided the best ensemble clustering results for all data sets.
KW - Consensus function
KW - Ensemble clustering
KW - FastMap
KW - High-dimensional data
UR - http://www.scopus.com/inward/record.url?scp=85009967011&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85009967011&partnerID=8YFLogxK
U2 - 10.14257/ijdta.2016.9.12.28
DO - 10.14257/ijdta.2016.9.12.28
M3 - Article
AN - SCOPUS:85009967011
SN - 2005-4270
VL - 9
SP - 311
EP - 330
JO - International Journal of Database Theory and Application
JF - International Journal of Database Theory and Application
IS - 12
ER -