<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Although Clustering Algorithm Based on Sparse Feature Vector (CABOSFV) and its related algorithms are efficient for high dimensional sparse data clustering, there exist several imperfections. Such imperfections as subjective parameter designation and order sensibility of clustering process would eventually aggravate the time complexity and quality of the algorithm. This paper proposes a parameter adjustment method of Bidirectional CABOSFV for optimization purpose. By optimizing Parameter Vector (PV) and Parameter Selection Vector (PSV) with the objective function of clustering validity, an improved Bidirectional CABOSFV algorithm using simulated annealing is proposed, which circumvents the requirement of initial parameter determination. The experiments on UCI data sets show that the proposed algorithm, which can perform multi-adjustment clustering, has a higher accurateness than single adjustment clustering, along with a decreased time complexity through iterations.</p></abstract>

Although Clustering Algorithm Based on Sparse Feature Vector (CABOSFV) and its related algorithms are efficient for high dimensional sparse data clustering, there exist several imperfections. Such imperfections as subjective parameter designation and order sensibility of clustering process would eventually aggravate the time complexity and quality of the algorithm. This paper proposes a parameter adjustment method of Bidirectional CABOSFV for optimization purpose. By optimizing Parameter Vector (PV) and Parameter Selection Vector (PSV) with the objective function of clustering validity, an improved Bidirectional CABOSFV algorithm using simulated annealing is proposed, which circumvents the requirement of initial parameter determination. The experiments on UCI data sets show that the proposed algorithm, which can perform multi-adjustment clustering, has a higher accurateness than single adjustment clustering, along with a decreased time complexity through iterations.

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

Donlinks School of Economics and Management, University of Science and Technology Beijing (USTB), Beijing,

School of Business, Anhui University, Hefei,

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Although Clustering Algorithm Based on Sparse Feature Vector (CABOSFV) and its related algorithms are efficient for high dimensional sparse data clustering,...

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

Pubblicato online: 25 gen 2017

Pagine: 27 - 42

DOI: https://doi.org/10.1515/cait-2016-0075

Parole chiave
Data mining, high dimensional sparse data, simulated annealing, clustering validity

© 2016 Minghan Yang et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Pubblicato online: 25 gen 2017

Pagine: 27 - 42

DOI: https://doi.org/10.1515/cait-2016-0075

Parole chiaveData mining, high dimensional sparse data, simulated annealing, clustering validity

© 2016 Minghan Yang et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Data mining, high dimensional sparse data, simulated annealing, clustering validity