Research of Potential Inclined Intrusion Data Mining Method in Large Network

Network intrusion is shown in more and more concealment, and some intrusion data is potential with inclination property. This paper is aimed to mine the potential inclined intrusion data effectively, and ensure the security of large network. On the basis of the traditional fractional Fourier transform data mining method. An improved potential inclined intrusion accurate data mining algorithm is proposed. New algorithm can separate the time and frequency coupling effectively. The discrete fractional Fourier transform is implemented for the network intrusion data firstly. The data is gathered in the fractional Fourier domain, the inclined intrusion data accumulation is increased. The network signal interference is suppressed effectively. Simulation results show that the proposed data mining algorithm can extract the potential inclined intrusion data in strong concealment. The mining performance is much better than the traditional algorithm, and it can be applied in the network security defense area perfectly.

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New Data Mining Method based on Probabilistic-Possibilistic-Mean (Discrete Data Mining Algorithm)

2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) ◽

10.1109/icccis48478.2019.8974501 ◽

2019 ◽

Author(s):

Anil Kumar Tiwari ◽

G. Ramakrishna ◽

Lokesh Kumar Sharma ◽

Sunil Kumar Kashyap

Keyword(s):

Data Mining ◽

Discrete Data ◽

Data Mining Algorithm ◽

Mining Method ◽

Data Mining Method ◽

Mining Algorithm

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DATA MINING ALGORITHM C4.5 CLASSIFICATION DETERMINATION CREDIT ELIGIBILITY FOR JAYA BERSAMA COOPERATIVES (KORJABE)

JURTEKSI ◽

10.33330/jurteksi.v8i1.1298 ◽

2021 ◽

Vol 8 (1) ◽

pp. 59-68

Author(s):

Christnatalis Christnatalis ◽

Roni Rayandi Saragih ◽

Bobby Christianto Tambunan

Keyword(s):

Data Mining ◽

Test Data ◽

Selection Method ◽

Training Data ◽

Classification Error ◽

Data Mining Algorithm ◽

Mining Method ◽

Data Mining Method ◽

Mining Algorithm ◽

C4.5 Algorithm

Abstract: This study uses the C4.5 classification algorithm to determine creditworthness, clasification aims to divide the assigned object intoin a number of categories called classes. In this study, the authorusing data mining and C4.5 algorithm as the selection method. The criteria used are loan installments, prospective customer income, termloan time, status of prospective customers. This study resulted in a classification modeldecision tree using the C4.5 algorithm is included in the Excellent category Classification with an accuracy value of 98.33% and a classification error of 1.67%,so that this study uses 70% training data and 30% test data. From resultthe calculation obtained shows that the C4.5 algorithm can be usedto determine the feasibility of granting credit to Koperasi Jaya customers Together (KORJABE). Keywords: Analysis, Credit Eligibility, C4 Algorithm, Data Mining, Method Abstrak: Penelitian ini menggunakan metode Algoritma C4.5 klasifikasi untuk menentukan kelayakan kredit, klasifikasi bertujuan untuk membagi objek yang ditetapkan ke dalam satu nomor kategori yang disebut kelas. Dalam penelitian ini, penulis menggunankan data mining dan algoritma C4.5 sebagai metode pemilihannya. Kriteria yang digunakan yaitu , angsuran pinjaman,penghasilan calon nasabah,jangka waktu pinjaman ,status calon nasabah. Penelitian ini menghasillkan model klasifikasi pohon keputusan menggunakan algoritma C4.5 termasuk dalam kategori Excellent Classification dengan nilai akurasi sebesar 98,33% dan klasifikasi eror 1,67%, sehingga penelitian ini kan menggunakan data latih 70% dan data uji 30%. Dari hasil perhitungan yang diperoleh menunjukan bahwa algoritma C4.5 dapat digunakan untuk menen tukan kelayakan pemberian kredit kepada nasabah Koperasi Jaya Bersama (KORJABE). Kata kunci: Algoritma C4.5, Analisis, Data Mining, Kelayakan Kredit, Metode

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