Bidirectional CABOSFV for high dimensional sparse data clustering

Gao, Xuedong; Yang, Minghan; Li, Ling

doi:10.1109/liss.2016.7854473

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Cabosfv Clustering Algorithms1

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Cited by 4 publications

(2 citation statements)

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“…Restricted by the unidirectionality, each adjustment needs to start over and cannot make use of the previous results, which considerably increases the computational complexity and limits the feasibility of optimization through iterations. Gao, Y a n g and Li [12] proposed Bidirectional CABOSFV by defining Bidirectional Sparse Feature Vector (B-SFV) and addition-subtraction of B-SFVs, which improved the performance of clustering through multiple adjustments, but gave no method of parameter optimization.…”

Section: Cabosfv Clustering Algorithmsmentioning

confidence: 99%

Improved Bidirectional CABOSFV Based on Multi-Adjustment Clustering and Simulated Annealing

Yang

Gao

2016

Cybernetics and Information Technologies

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Section: Cabosfv Clustering Algorithmsmentioning

confidence: 99%

Improved Bidirectional CABOSFV Based on Multi-Adjustment Clustering and Simulated Annealing

Yang

Gao

2016

Cybernetics and Information Technologies

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“…Bidirectional CABOSFV [6] proposes Bidirectional Sparse Feature Vector (B-SFV), which can be calculated incrementally and accurately when clusters are split. The clustering result of B-CABOSFV can be adjusted by using B-SFV.…”

Section: Introductionmentioning

confidence: 99%

ADJ-CABOSFV for High Dimensional Sparse Data Clustering

Wu¹,

Gao²,

Liu³

2017

dtem

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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.

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Hierarchical Clustering Algorithm for Binary Data Based on Cosine Similarity

Gao

2018

2018 8th International Conference on Logistics, Informatics and Service Sciences (LISS)

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Bidirectional CABOSFV for high dimensional sparse data clustering

Cited by 4 publications

References 5 publications

Improved Bidirectional CABOSFV Based on Multi-Adjustment Clustering and Simulated Annealing

Improved Bidirectional CABOSFV Based on Multi-Adjustment Clustering and Simulated Annealing

ADJ-CABOSFV for High Dimensional Sparse Data Clustering

Hierarchical Clustering Algorithm for Binary Data Based on Cosine Similarity

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