Algorithms for clustering data
Algorithms for clustering data
Communications of the ACM
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Variable Precision Rough Sets with Asymmetric Bounds
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Growing subspace pattern recognition methods and their neural-network models
IEEE Transactions on Neural Networks
Enhancing quality of knowledge synthesized from multi-database mining
Pattern Recognition Letters
Dominance-based rough set approach and knowledge reductions in incomplete ordered information system
Information Sciences: an International Journal
Rough-DBSCAN: A fast hybrid density based clustering method for large data sets
Pattern Recognition Letters
Prefix-suffix trees: a novel scheme for compact representation of large datasets
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
A logarithmic time complexity algorithm for pattern searching using product-sum property
Computers & Mathematics with Applications
Expanding tolerance RST models based on cores of maximal compatible blocks
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Multi-granulation fuzzy rough sets
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In data mining, an important goal is to generate an abstraction of the data. Such an abstraction helps in reducing the space and search time requirements of the overall decision making process. Further, it is important that the abstraction is generated from the data with a small number of disk scans. We propose a novel data structure, pattern count tree (PC-tree), that can be built by scanning the database only once. PC-tree is a minimal size complete representation of the data and it can be used to represent dynamic databases with the help of knowledge that is either static or changing. We show that further compactness can be achieved by constructing the PC-tree on segmented patterns. We exploit the flexibility offered by rough sets to realize a rough PC-tree and use it for efficient and effective rough classification. To be consistent with the sizes of the branches of the PC-tree, we use upper and lower approximations of feature sets in a manner different from the conventional rough set theory. We conducted experiments using the proposed classification scheme on a large-scale hand-written digit data set. We use the experimental results to establish the efficacy of the proposed approach.