Data Mining Lab.
Welcome to Data Mining Laboratory in the Department of Computer Science and Engineering at Seoul National University. Our research interests lie in big data mining which aims to find models, algorithms, and systems for scalable data analysis with applications on knowledge discovery, learning, and anomaly detection. Specifically, we focus on the following research topics: deep learning & scalable machine learning, recommendation system, big graph mining, big tensor mining, online stream mining, and anomaly/fraud detection.
What's New
-
[Jan 2019] M.S/Ph.D. student Jaemin Yoo in DM Lab. won HumanTech Award.
M.S/Ph.D. student Jaemin Yoo won the HumanTech award (honorable mention, 4th in Computer Science). HumanTech award is given to best papers in the engineering area, and it is the most prestigious award among such kind in Korea. Congratulations!
-
[Jan 2019] Postdoctoral researcher Ha-Myung Park will join Kookmin University as an Assistant Professor
Postdoctoral researcher Ha-Myung Park will join Kookmin University as an Assistant Professor, starting from March 1st, 2019. Congraturations!
-
[Nov 2018] Prof. Kang won the Most Impacted Paper Award (10-year highest impact paper award) from IEEE International Conference on Data Mining (ICDM).
Prof. Kang won the Most Impacted Paper Award (10-year highest impact paper award) from IEEE International Conference on Data Mining (ICDM). The award is given to the highest impacted paper in the area of Data Mining for the last 10 years. Congratulations!
-
[Aug 2018] A paper accepted to CIKM 2018, a top tier data mining conference.
A paper is accepted to CIKM 2018, a top tier data mining conference. The paper "Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range" proposed an algorithm which extracts key patterns of multiple time series data in an arbitrary time range.
-
[Jun 2018] Jinhong Jung won BK21 Plus Excellent Research Award.
Ph.D. student Jinhong Jung won the BK21 Plus Excellent Research Award. BK21 Plus Excellent Research Award is given to excellent graduate students in the Engineering area. Congratulations!
Featured Projects
SIDE
SIDE is a general network embedding method that represents both sign and direction of edges in the embedding space.
PegasusN
PegasusN is a scalable and versatile graph mining system that runs on Hadoop and Spark.
ZoomSVD
Zoom-SVD is a fast and memory efficient method for finding latent factors of time series data in an arbitrary time range.
P-Tucker
P-Tucker is a scalable Tucker factorization method for sparse tensors, which updates factor matrices in a row-wise manner.
SBP
Supervised Belief Propagation (SBP) is a supervised and scalable method for classifying unknown nodes in an attributed network.
BIGtensor
BIGtensor is a large-scale, distributed tensor mining library that provides various operations for handling tensor and tensor decompositions.
BePI
BePI is a scalable, accurate, and fast method for computing RWR on billion-scale graphs.
Data Mining Seminar
- Every Tuesday, 18:10 - 19:00
- #551, Building 301, Seoul National University
-
Mar 12 Jun-Gi Jang
Neural Tensor Factorization for Temporal Interaction Learning
-
Mar 5 Seungcheol Park
Fast Range Image-Based Segmentation of Sparse 3D Laser Scans for Online Operation
-
Feb 18 Jaemin Yoo
Revisiting Semi-Supervised Learning with Graph Embeddings
-
Feb 11 Jinhong Jung
Modeling Real-world Networks: Stochastic Kronecker Graph and R-MAT
-
Jan 28 Sumin Hong
Mask R-CNN