TY - GEN
T1 - Deep data fuzzy clustering
AU - Przybyła, Tomasz
AU - Pander, Tomasz
AU - Czabański, Robert
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/3/20
Y1 - 2014/3/20
N2 - In this paper we present a clustering method called Deep Data clustering. The idea of the proposed method is based on a decomposition of an input dataset. The aim od the decomposition (or dimensionality reduction) process is to reveal internal data structures in the dataset. Two methods are selected for this purpose: the principal component analysis (PCA) and the Fisher linear discriminant (FLD). The reduction process is repeated as long as the number of features is equal to one. Meanwhile, the clustering procedure is applied for the each reduced dataset. Finally, based on the clustering results obtained for the reduced datasets, the input dataset is clustered by applying the collaborative fuzzy clustering method. The well known Pima and Iris databases are used in conducted numerical experiment. The obtained results show usefulness of the proposed approach.
AB - In this paper we present a clustering method called Deep Data clustering. The idea of the proposed method is based on a decomposition of an input dataset. The aim od the decomposition (or dimensionality reduction) process is to reveal internal data structures in the dataset. Two methods are selected for this purpose: the principal component analysis (PCA) and the Fisher linear discriminant (FLD). The reduction process is repeated as long as the number of features is equal to one. Meanwhile, the clustering procedure is applied for the each reduced dataset. Finally, based on the clustering results obtained for the reduced datasets, the input dataset is clustered by applying the collaborative fuzzy clustering method. The well known Pima and Iris databases are used in conducted numerical experiment. The obtained results show usefulness of the proposed approach.
KW - Data clustering
KW - Fisher linear discriminant
KW - Fuzzy collaborative clustering
KW - Principal component analysis
UR - https://www.scopus.com/pages/publications/84949928936
U2 - 10.1109/ITAIC.2014.7065020
DO - 10.1109/ITAIC.2014.7065020
M3 - Conference contribution
AN - SCOPUS:84949928936
T3 - 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2014
SP - 130
EP - 134
BT - 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 7th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2014
Y2 - 20 December 2014 through 21 December 2014
ER -