Implementasi Algoritma FP-Growth Dalam Penentuan Pola Hubungan Kecelakaan Lalu Lintas

Klasifikasi Data Cardiotocography Dengan Integrasi Metode Neural Network Dan Particle Swarm Optimization

Backpropagation (BP) adalah sebuah metode yang digunakan dalam training Neural Network (NN) untuk menentukan parameter bobot yang sesuai. Proses penentuan parameter bobot dengan menggunakan metode backpropagation sangat dipengaruhi oleh pemilihan nilai learning rate (LR)-nya. Penggunaan nilai learning rate yang kurang optimal berdampak pada waktu komputasi yang lama atau akurasi klasifikasi yan...

nonordfp: An FP-growth variation without rebuilding the FP-tree

We describe a frequent itemset mining algorithm and implementation based on the well-known algorithm FPgrowth. The theoretical difference is the main data structure (tree), which is more compact and which we do not need to rebuild for each conditional step. We thoroughly deal with implementation issues, data structures, memory layout, I/O and library functions we use to achieve comparable perfo...

Thymineless death in polA+ and polA- strains of Escherichia coli.

The extreme sensitivity of polA(-) cells to thymineless death is due, primarily, to the absence of an extended lag prior to the commencement of death. Once thymineless death has commenced, the rate in polA(-) cells is only slightly faster than in polA(+) cells.

Project Proposal Student : Lalu Luka

MS-FP-Growth: A multi-support Vrsion of FP-Growth Agorithm

In this paper, we propose a new version of FP-Growth algorithm to find association rules. In this version, we vary the minsup value from one level to another. This variation is made in two cases: increasing and decreasing the minsup value. We performed a set of experiments to validate the usefulness of our proposition in the generation of association rules process.