TY - GEN
T1 - TWave
T2 - 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
AU - Barnathan, Michael
AU - Megalooikonomou, Vasileios
AU - Faloutsos, Christos
AU - Mohamed, Feroze B.
AU - Faro, Scott
PY - 2010
Y1 - 2010
N2 - Recent advances in data acquisition and sharing have made available large quantities of complex data in which features may have complex interrelationships or may not be scalar. For such datasets, the traditional matrix model is no longer appropriate and may fail to capture relationships between features or fail to discover the underlying concepts that features represent. These datasets are better modeled using tensors, which are high-order generalizations of matrices. However, naive tensor algorithms suffer from poor efficiency and may fail to consider spatiotemporal neighborhood relationships in analysis. To surmount these difficulties, we propose TWave, a wavelet and tensor-based methodology for automatic summarization, classification, concept discovery, clustering, and compression of complex datasets. We also derive TWaveCluster, a novel high-order clustering approach based on WaveCluster, and compare our approach against WaveCluster and k-means. The efficiency of our method is competitive with WaveCluster and significantly outperforms k-means. TWave consistently outperformed competitors in both speed and accuracy on a 9.3 GB medical imaging dataset. Our results suggest that a combined wavelet and tensor approach such as TWave may be successfully employed in the analysis of complex high-order datasets.
AB - Recent advances in data acquisition and sharing have made available large quantities of complex data in which features may have complex interrelationships or may not be scalar. For such datasets, the traditional matrix model is no longer appropriate and may fail to capture relationships between features or fail to discover the underlying concepts that features represent. These datasets are better modeled using tensors, which are high-order generalizations of matrices. However, naive tensor algorithms suffer from poor efficiency and may fail to consider spatiotemporal neighborhood relationships in analysis. To surmount these difficulties, we propose TWave, a wavelet and tensor-based methodology for automatic summarization, classification, concept discovery, clustering, and compression of complex datasets. We also derive TWaveCluster, a novel high-order clustering approach based on WaveCluster, and compare our approach against WaveCluster and k-means. The efficiency of our method is competitive with WaveCluster and significantly outperforms k-means. TWave consistently outperformed competitors in both speed and accuracy on a 9.3 GB medical imaging dataset. Our results suggest that a combined wavelet and tensor approach such as TWave may be successfully employed in the analysis of complex high-order datasets.
KW - Matrix models
KW - Spatiotemporal mining
KW - Tensors
KW - Wavelets
UR - http://www.scopus.com/inward/record.url?scp=79956330151&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79956330151&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13657-3_28
DO - 10.1007/978-3-642-13657-3_28
M3 - Conference contribution
AN - SCOPUS:79956330151
SN - 3642136567
SN - 9783642136566
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 246
EP - 253
BT - Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
Y2 - 21 June 2010 through 24 June 2010
ER -