C4.5: programs for machine learning
C4.5: programs for machine learning
Independent component analysis: theory and applications
Independent component analysis: theory and applications
New quadric metric for simplifiying meshes with appearance attributes
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
The digital Michelangelo project: 3D scanning of large statues
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Data mining: concepts and techniques
Data mining: concepts and techniques
SVD and Signal Processing II: Algorithms, Analysis and Applications
SVD and Signal Processing II: Algorithms, Analysis and Applications
Machine Learning
Incremental PCA or On-Line Visual Learning and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective and Efficient Dimensionality Reduction for Large-Scale and Streaming Data Preprocessing
IEEE Transactions on Knowledge and Data Engineering
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Foraging theory for dimensionality reduction of clustered data
Machine Learning
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Each year, millions of people suffer from after-effects of pipeline leakages, spills, and eruptions. Leakages Detection Systems (LDS) are often used to understand and analyse these phenomena but unfortunately could not offer complete solution to reducing the scale of the problem. One recent approach was to collect datasets from these pipeline sensors and analyse offline, the approach yielded questionable results due to vast nature of the datasets. These datasets together with the necessity for powerful exploration tools made most pipelines operating companies "data rich but information poor". Researchers have therefore identified problem of dimensional reduction for pipeline sensor datasets as a major research issue. Hence, systematic gap filling data mining development approaches are required to transform data "tombs" into "golden nuggets" of knowledge. This paper proposes an algorithm for this purpose based on the Incremental Orthogonal Centroid (IOC). Search time for specific data patterns may be enhanced using this algorithm.