TY  - JOUR
T1  - GUSTAFSON-KESSEL AND FUZZY C-MEANS ALGORITHMS BY COLON CANCER DATA IN FUZZY CLUSTERING
TT  - GUSTAFSON-KESSEL AND FUZZY C-MEANS ALGORITHMS BY COLON CANCER DATA IN FUZZY CLUSTERING
AU  - Ozdemir, Ozer
AU  - Kaya, Asli
PY  - 2018
DA  - July
JF  - Technological Applied Sciences
JO  - NWSA
PB  - E-Journal of New World Sciences Academy
WT  - DergiPark
SN  - 1308-7223
SP  - 243
EP  - 252
VL  - 13
IS  - 3
LA  - en
AB  - Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. For the analysis of microarray data, clustering techniques are frequently used. So in this study, in cases where classical clustering analysis is insufficient to analyze data, fuzzy c-means algorithm and Gustafson-Kessel algorithm, which are improved to supply with advancing alternative statistical methods, are used. Firstly, the number of the optimum cluster was decided since the number of the cluster was not known at the beginning. Then, validity indexes and elbow criterion are applied to find the optimal number of clusters for both algorithms. It is seen that for both algorithms, the elbow was situated in the c=3 position as a result of the experimental result. At the end of the study, it is graphically stated that the fuzzy cmeans algorithm is getting better clusters for the colon cancer dataset.
KW  - Fuzzy Clustering
KW  - Fuzzy C-Means Algorithm
KW  - Cancer
KW  - Gustafson-Kessel Algorithm
KW  - Colon Cancer Data
N2  - Microarraytechnology has made it possible to simultaneously measure the expression levelsof large numbers of genes in a short time. For the analysis of microarray data,clustering techniques are frequently used. So in this study, in cases whereclassical clustering analysis is insufficient to analyze data, fuzzy c-meansalgorithm and Gustafson-Kessel algorithm, which are improved to supply withadvancing alternative statistical methods, are used. Firstly, the number of theoptimum cluster was decided since the number of the cluster was not known atthe beginning. Then, validity indexes and elbow criterion are applied to findthe optimal number of clusters for both algorithms. It is seen that for bothalgorithms, the elbow was situated in the c=3 position as a result of theexperimental result. At the end of the study, it is graphically stated that thefuzzy c-means algorithm is getting better clusters for the colon cancer dataset.
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UR  - https://dergipark.org.tr/tr/pub/nwsatecapsci/issue//350542
L1  - https://dergipark.org.tr/tr/download/article-file/511972
ER  -