Classification algorithms
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Proceedings of the 2002 ACM symposium on Applied computing
Deriving High Confidence Rules from Spatial Data Using Peano Count Trees
WAIM '01 Proceedings of the Second International Conference on Advances in Web-Age Information Management
P-tree classification of yeast gene deletion data
ACM SIGKDD Explorations Newsletter
Efficient OLAP operations for spatial data using peano trees
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
PINE: Podium Incremental Neighbor Evaluator for classifying spatial data
Proceedings of the 2003 ACM symposium on Applied computing
An optimized approach for KNN text categorization using P-trees
Proceedings of the 2004 ACM symposium on Applied computing
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A vertical distance-based outlier detection method with local pruning
Proceedings of the thirteenth ACM international conference on Information and knowledge management
SMART-TV: a fast and scalable nearest neighbor based classifier for data mining
Proceedings of the 2006 ACM symposium on Applied computing
Comparison of the nearest feature classifiers for face recognition
Machine Vision and Applications
Parameter optimized, vertical, nearest-neighbor-vote and boundary-based classification
ACM SIGKDD Explorations Newsletter
An efficient weighted nearest neighbour classifier using vertical data representation
International Journal of Business Intelligence and Data Mining
Extensions of the k Nearest Neighbour methods for classification problems
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
A hybrid artificial intelligent-based criteria-matching with classification algorithm
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Classification based on logical concept analysis
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Using sub-sequence information with kNN for classification of sequential data
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
A new fuzzy classifier for data streams
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Predicate-tree based pretty good privacy of data
CMS'12 Proceedings of the 13th IFIP TC 6/TC 11 international conference on Communications and Multimedia Security
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Classification of spatial data streams is crucial, since the training dataset changes often. Building a new classifier each time can be very costly with most techniques. In this situation, k-nearest neighbor (KNN) classification is a very good choice, since no residual classifier needs to be built ahead of time. KNN is extremely simple to implement and lends itself to a wide variety of variations. We propose a new method of KNN classification for spatial data using a new, rich, data-mining-ready structure, the Peano-count-tree (P-tree). We merely perform some AND/OR operations on P-trees to find the nearest neighbors of a new sample and assign the class label. We have fast and efficient algorithms for the AND/OR operations, which reduce the classification time significantly. Instead of taking exactly the k nearest neighbors we form a closed-KNN set. Our experimental results show closed-KNN yields higher classification accuracy as well as significantly higher speed.