TY - GEN
T1 - A novel information-theoretic clustering algorithm for robust, unsupervised classification
AU - Temel, 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 fixed-threshold, 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 fixed-threshold, 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/51549085086
U2 - 10.1109/ISSPA.2007.4555489
DO - 10.1109/ISSPA.2007.4555489
M3 - Conference contribution
AN - SCOPUS:51549085086
SN - 1424407796
SN - 9781424407798
T3 - 2007 9th International Symposium on Signal Processing and its Applications, ISSPA 2007, Proceedings
BT - 2007 9th International Symposium on Signal Processing and its Applications, ISSPA 2007, Proceedings
T2 - 2007 9th International Symposium on Signal Processing and its Applications, ISSPA 2007
Y2 - 12 February 2007 through 15 February 2007
ER -