CABOSFV algorithm for high dimensional sparse data clustering
2004
An algorithm, Clustering Algorithm Based On Sparse Feature Vector (CABOSFV), was proposed for the high dimensional clustering of binary sparse data. This algorithm compresses the data effectively by using a tool 'Sparse Feature Vector', thus reduces the data scale enormously, and can get the clustering result with only one data scan. Both theoretical analysis and empirical tests showed that CABOSFV is of low computational complexity. The algorithm finds clusters in high dimensional large datasets efficiently and handles noise effectively.
Keywords:
- k-medians clustering
- Correlation clustering
- Cluster analysis
- Data stream clustering
- FLAME clustering
- Linde–Buzo–Gray algorithm
- Canopy clustering algorithm
- Algorithm
- Machine learning
- Computer science
- CURE data clustering algorithm
- Artificial intelligence
- Pattern recognition
- K-SVD
- Sparse approximation
- Determining the number of clusters in a data set
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