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- research-articleAugust 2017
AGILE: Augmented GrId based cLustEring
ICICM '17: Proceedings of the 7th International Conference on Information Communication and ManagementPages 5–11https://doi.org/10.1145/3134383.3134412Clustering of large databases is still a challenge, especially with high dimensional inputs. Conventional clustering methods suffer from high sensitivity to noisy samples and bad initial conditions. To overcome the mentioned drawbacks, a new kernel-based ...
- ArticleMarch 2010
Minimizing energy consumption with probabilistic distance models in wireless sensor networks
Minimizing energy consumption in wireless sensor networks has been a challenging issue, and grid-based clustering and routing schemes have attracted a lot of attention due to their simplicity and feasibility. Thus how to determine the optimal grid size ...
- ArticleAugust 2009
GOD-CS: A New Grid-Oriented Dissection Clustering Scheme for Large Databases
ADMA '09: Proceedings of the 5th International Conference on Advanced Data Mining and ApplicationsPages 302–313https://doi.org/10.1007/978-3-642-03348-3_30Many recent clustering schemes have been applied in previous investigations to resolve the issues of high execution cost and low correction ratio in arbitrary shapes. Two conventional approaches that can solve one of these problems accurately are K-...
- articleJune 2009
A new approach for clustering gene expression time series data
International Journal of Bioinformatics Research and Applications (IJBRA), Volume 5, Issue 3Pages 310–328https://doi.org/10.1504/IJBRA.2009.026422Identifying groups of genes that manifest similar expression patterns is crucial in the analysis of gene expression time series data. Choosing a similarity measure to determine the similarity or distance between profiles is an important task. This paper ...
- ArticleMay 2009
FARM: a new efficient and effective data clustering algorithm
MUSP'09: Proceedings of the 9th WSEAS international conference on Multimedia systems & signal processingPages 253–258This investigation presents a method named FARM that combines a grid-based algorithm with the density-based approach for clustering data in data mining applications. In the FARM clustering method, the number of separate clusters need not be specified ...
- ArticleMay 2009
GF-DBSCAN: a new efficient and effective data clustering technique for large databases
MUSP'09: Proceedings of the 9th WSEAS international conference on Multimedia systems & signal processingPages 231–236The DBSCAN data clustering accurately searches adjacent area with similar density of data, and effectively filters noise, making it very valuable in data mining. However, DBSCAN needs to compare all data in each object, making it very time-consuming. ...
- ArticleApril 2009
LILA: A Connected Components Labeling Algorithm in Grid-Based Clustering
DBTA '09: Proceedings of the 2009 First International Workshop on Database Technology and ApplicationsPages 213–216https://doi.org/10.1109/DBTA.2009.144Labeling the connected components in the feature space is an important step in grid based clustering algorithms in data mining. Although Connected Components LabelingAlgorithms have been highly improved in image processing domain, there is little ...
- posterOctober 2008
A coarse-grain grid-based subspace clustering method for online multi-dimensional data streams
CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge managementPages 1521–1522https://doi.org/10.1145/1458082.1458366This paper proposes a subspace clustering algorithm which combines grid-based clustering with frequent itemset mining. Given a d-dimensional data stream, the on-going distribution statistics of its data elements in every one-dimensional data space is ...
- articleApril 2008
A deflected grid-based algorithm for clustering analysis
The grid-based clustering algorithm, which partitions the data space into a finite number of cells to form a grid structure and then performs all clustering operations on this obtained grid structure, is an efficient clustering algorithm, but its effect ...
- research-articleNovember 2007
Grid-based subspace clustering over data streams
CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge managementPages 801–810https://doi.org/10.1145/1321440.1321551A real-life data stream usually contains many dimensions and some dimensional values of its data elements may be missing. In order to effectively extract the on-going change of a data stream with respect to all the subsets of the dimensions of the data ...
- ArticleSeptember 2007
Network snomaly detection based on semi-supervised clustering
SMO'07: Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and OptimizationPages 440–443A semi-supervised clustering algorithm based on the traditional k-means algorithm is proposed for network anomaly detection. We improve the original algorithm mainly in three aspects. First, the number of clusters is automatically decided by merging and ...
- ArticleMay 2007
Grid-based clustering algorithm based on intersecting partition and density estimation
In order to solve the problem that traditional grid-based clustering techniques lack of the capability of dealing with data of high dimensionality, we propose an intersecting grid partition method and a density estimation method. The partition method ...
- ArticleMay 2007
Approximate trace of grid-based clusters over high dimensional data streams
PAKDD'07: Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data miningPages 753–760Clustering in a large data set of high dimensionality has always been a serious challenge in the field of data mining. A good clustering method should provide flexible scalability to the number of dimensions as well as the size of a data set. We have ...
