ADJ-CABOSFV for High Dimensional Sparse Data Clustering
2017
The classic algorithm for high dimensional sparse data clustering, CABOSFV, cannot adjust the sets once generated, which leads to the final clustering result impacted by the preceding clustering result. This paper proposes ADJ-CABOSFV that can adjust the sets clustered by CABOSFV and the objects in the same set clustered by ADJ-CABOSFV are more similar without increasing the number of parameters. The experiments on UCI data sets show that ADJ-CABOSFV maintains superiority on high-dimensional sparse data of binary variables, and the clustering quality is better than the classic CABOSFV.
Keywords:
- Fuzzy clustering
- k-medians clustering
- Correlation clustering
- FLAME clustering
- Data stream clustering
- Cluster analysis
- Machine learning
- Canopy clustering algorithm
- Computer science
- Pattern recognition
- CURE data clustering algorithm
- Artificial intelligence
- Constrained clustering
- Data mining
- Single-linkage clustering
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