Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing and computer vision: an introduction to theory and implementations
Digital image processing and computer vision: an introduction to theory and implementations
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Large-Scale Parallel Data Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
IEEE Transactions on Knowledge and Data Engineering
Clustering and Classification in Structured Data Domains Using Fuzzy Lattice Neurocomputing (FLN)
IEEE Transactions on Knowledge and Data Engineering
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Algorithm to Cluster High Dimensional Basket Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
'1 + 1 2': Merging Distance and Density Based Clustering
DASFAA '01 Proceedings of the 7th International Conference on Database Systems for Advanced Applications
P-AutoClass: Scalable Parallel Clustering for Mining Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
Clustering using a coarse-grained parallel genetic algorithm: a preliminary study
CAMP '95 Proceedings of the Computer Architectures for Machine Perception
Polynomial time approximation schemes for geometric k-clustering
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Scaling genetically guided fuzzy clustering
ISUMA '95 Proceedings of the 3rd International Symposium on Uncertainty Modelling and Analysis
A survey of data mining and knowledge discovery software tools
ACM SIGKDD Explorations Newsletter
Clustering Large Datasets in Arbitrary Metric Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
Vector quantization and clustering: a pyramid approach
DCC '95 Proceedings of the Conference on Data Compression
A New Data Clustering Approach for Data Mining in Large Databases
ISPAN '02 Proceedings of the 2002 International Symposium on Parallel Architectures, Algorithms and Networks
IEEE Transactions on Knowledge and Data Engineering
Dual Clustering: Integrating Data Clustering over Optimization and Constraint Domains
IEEE Transactions on Knowledge and Data Engineering
Multi-scale spline-based contour data compression and reconstruction through curvature scale space
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
A clustering based approach to perceptual image hashing
IEEE Transactions on Information Forensics and Security
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In this paper, we devise a novel algorithm for large data set clustering. Our algorithm utilizes efficient image processing techniques to cluster the data set after mapping its points into a binary image map. To this end, the algorithm avoids exhaustive search by using the mapped image, which contain the critical boundary information needed to detect clusters. Compared to available data clustering techniques, the proposed algorithm produces similar quality results and outperforms them in execution time and storage requirements.