Y. Chen, S. Tang, N. Bouguila, C. Wang, J. Du, and H. L. Li, "A fast clustering algorithm based on pruning unnecessary distance computations in dbscan for high-dimensional data," Pattern Recognition, 2018 (https://doi.org/10.1016/j.patcog.2018.05.030.)....
Improved Method for Noise Detection by DBSCAN and Angle Based Outlier Factor in High Dimensional DatasetsDBSCANClusteringOutlier detectionPrincipal component analysisABODVarious data mining methods are used to detect outliers from different databases. It is essential to detect outliers in different kinds of...
We present a new result concerning the parallelisation of DBSCAN, a Data Mining algorithm for density-based spatial clustering. The overall structure of DBSCAN has been mapped to a skeletonstructured program that performs parallel exploration of each cluster. The approach is useful to improve perform...
You are working in a consulting company as a data scientist. The project you were currently assigned to has data from students who have recently finished courses about finances. The financial company that conducts the courses wants to understand if there are common factors that influence students t...
Although researchers have been working on clustering algorithms for decades, and a lot of algorithms for clustering have been developed, there is still no efficient algorithm for clustering very large databases and high dimensional data. As an outstanding representative of clustering algorithms, DBSCAN ...
Ertöz, Levent, Michael Steinbach, and Vipin Kumar. 2003. “Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data.” InProceedings of the 2003 SIAM International Conference on Data Mining (SDM), 47–58.https://doi.org/10.1137/1.9781611972733.5. ...
We can quickly find the nearest neighbors of a data point from a large number of high-dimensional data sets with the help of LSH index. Aiming at the efficiency problem of DBSCAN algorithm, this paper proposes two improved algorithms, LSH-DBSCAN and LSHSNN-DBSCAN which combine with locality-...
DBSCAN algorithm can achieve cluster of any shape of dataset, Fuzzy c-means is suitable for dataset which is uniform distribution around the cluster centers , CABoSFV algorithm can be a good clustering for high-dimensional dataset(such as WEB data). Embedding DBSCAN、FCM and CABoSFV three ...
20150046411 Managing and Querying Spatial Point Data in Column Stores 2015-02-12 Kazmaier et al. 20070156634 Multidimensional dynamic clustering (MDDC) 2007-07-05 Martin 20040225638 Method and system for data mining in high dimensional data spaces 2004-11-11 Geiselhart 20030004938 Method of storin...
Although researchers have been working on clustering algorithms for decades, and a lot of algorithms for clustering have been developed, there is still no efficient algorithm for clustering very large databases and high dimensional data. As an outstanding representative of clustering algorithms, DBSCAN ...