Classification Algorithm in Data Mining Based on Maximum Exponential Class Counts Technique

A new split attribute measure for decision tree node split during decision tree creation is proposed. The new split measure consists of the sum of class counts of distinct values of categorical attributes in the dataset. Larger counts induce larger partitions and smaller trees there by favors to the determination of the best spit attribute. The new split attribute measure is termed as maximum exponential class counts (MECC). Experiment results obtained over several UCI machine learning categorical datasets predominantly indicate that the decision tree models created based on the proposed MECC node split attribute technique provides better classification accuracy results and smaller trees in size than the decision trees created using popular gain ratio, normalized gain ratio and gini-index measures. The experimental results are mainly focused on performing and analyzing the results from the node splitting measures alone.

SKS-wave splitting and upper mantle structure: results in the Southern Apennines

Shear wave splitting measures at six temporary stations in the Southern Apennines are computed analyzing fifteen events recorded during the spring and summer 1996, with magnitude greater than 5.8. The splitting parameters were measured using Silver and Chan's (1991) method only on SKS and SKKS phases. Evidence for strong seismic anisotropy was found at all the stations: delay times dt are generally larger than 1.5 s and fast directions Ø are quite variable in different geodynamic domains. NW-SE Ø average directions are found at stations on the mountain belt while at stations on the foredeep and foreland Ø is on average N-S. Changes in splitting parameters may be related to upper mantle structure, and particularly to the geometry of a fragmented lithosphere subducting beneath the Apennines.