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. © 2007 IEEE.
CITATION STYLE
Temei, T., & Aydin, N. (2007). A threshold free clustering algorithm for robust unsupervised classification. In Proceedings - 2007 ECSIS Symposium on Bio-inspired, Learning, and Intelligent Systems for Security, BLISS 2007 (pp. 119–122). https://doi.org/10.1109/BLISS.2007.7
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