A single-step clustering algorithm based on a new information-theoretic sample association metric definition

Title:

A single-step clustering algorithm based on a new information-theoretic sample association metric definition

Creator:

Temel, T.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:912d487c-dfe9-44c9-bd03-14474d6d0bdb
uuid:912d487c-dfe9-44c9-bd03-14474d6d0bdb
doi:10.14311/NNW.2017.27.027

Subject:

clustering, clustering algorithms, information theory, mutual information, and unsupervised learning

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

A single-step information-theoretic algorithm that is able to identify possible clusters in dataset is presented. The proposed algorithm consists in representation of data scatter in terms of similarity-based data point entropy and probability descriptions. By using these quantities, an information-theoretic association metric called mutual ambiguity between data points is defined, which then is to be employed in determining particular data points called cluster identifiers. For forming individual clusters corresponding to cluster identifiers determined as such, a cluster relevance rule is defined. Since cluster identifiers and associative cluster member data points can be identified without recursive or iterative search, the algorithm is single-step. The algorithm is tested and justified with experiments by using synthetic and anonymous real datasets. Simulation results demonstrate that the proposed algorithm also exhibits more reliable performance in statistical sense compared to major algorithms.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

519-528

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2017 Volume:27 | Number:5

Harvested from:

CDK

Metadata only:

false