TY - GEN
T1 - A threshold free clustering algorithm for robust unsupervised classification
AU - Temei, Turgay
AU - Aydin, Nizamettin
PY - 2007
Y1 - 2007
N2 - A new information-theoretic, unsupervised, subtractive clustering algorithm is proposed. The algorithm eliminates threshold constraint to detect possible cluster members. Cluster centers are formed with minimum entropy. Instead of using a fixedthreshold, a decision region is formed with the use of maximum mutual information. Cluster members are chosen with a relative-cost assigned in partitions of data set. The algorithm yields more reliably distributed cluster numbers in statistical sense, hence reducing further computation for validation, which is justified for a set of synthetic data.
AB - A new information-theoretic, unsupervised, subtractive clustering algorithm is proposed. The algorithm eliminates threshold constraint to detect possible cluster members. Cluster centers are formed with minimum entropy. Instead of using a fixedthreshold, a decision region is formed with the use of maximum mutual information. Cluster members are chosen with a relative-cost assigned in partitions of data set. The algorithm yields more reliably distributed cluster numbers in statistical sense, hence reducing further computation for validation, which is justified for a set of synthetic data.
UR - https://www.scopus.com/pages/publications/46449085511
U2 - 10.1109/BLISS.2007.7
DO - 10.1109/BLISS.2007.7
M3 - Conference contribution
AN - SCOPUS:46449085511
SN - 0769529194
SN - 9780769529196
T3 - Proceedings - 2007 ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security, BLISS 2007
SP - 119
EP - 122
BT - Proceedings - 2007 ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security, BLISS 2007
T2 - 2007 ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security, BLISS 2007
Y2 - 9 August 2007 through 10 August 2007
ER -