Algorithms for clustering data
Algorithms for clustering data
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Clustering spatial data using random walks
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
WaveCluster: a wavelet-based clustering approach for spatial data in very large databases
The VLDB Journal — The International Journal on Very Large Data Bases
A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
Detecting region outliers in meteorological data
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
FAÇADE: a fast and effective approach to the discovery of dense clusters in noisy spatial data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Discovering spatial patterns accurately with effective noise removal
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Spatial contextual noise removal for post classification smoothing of remotely sensed images
Proceedings of the 2005 ACM symposium on Applied computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Spatial contextual classification and prediction models for mining geospatial data
IEEE Transactions on Multimedia
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Noise removal is perhaps one of the most fundamental and challenging tasks for extracting useful information from a spatial data set. One of the challenges is that there is no general agreement on the definition of noise that can be universally applied to all different domains. This paper proposes a novel technique called Visualization-Informed Noise Elimination (VINE) to support a customized noise removal through incorporation of domain knowledge. The VINE technique consists of three steps of consecutive operations. First, a k-mutual neighbor graph is derived from a spatial data set to model the spatial proximity among data points. Next, a fast partitioning method is employed to reassemble graph nodes into groups. Last, a 3-dimensional (3D) visualization model is created to provide a layered view of the partitioned data, which allows an informed identification and elimination of noise by tailoring to the requirements of a specific domain. The flexibility and customizability provided by this novel technique ensures an effective differentiation of noise from valid data and demonstrates various advantages over traditional methods with improved results. When adapted in post-classification smoothing of high-spatial-resolution remotely sensed images, this approach was able to discover and reassign noise (such as shadows often seen in high-spatial-resolution images) to its proper target class. By incorporating domain knowledge and making use of spatial contextual information, the VINE technique could produce results significantly superior to existing approaches such as majority filter and size-based noise removal.