Frequent Item Set Mining of Large Datasets Using CUDA Computing

Karthik, P.C.; Banu, J. Saira

doi:10.1007/978-981-15-0184-5_63

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Zhu (2018) menjelaskan item set dibangun dari masing-masing item variabel yang terdapat pada database, sebagai permulaan pencarian 1-item set yakni masing-masing dari empat variabel, misalnya ({personality}, {self-efficacy}, {hemisphere preference}, dan {self-directed learning}), kemudian masing-masing 1-item set dikombinasikan dengan 1-item set lainnya sehingga membentuk 2-item set, contohnya {personality, self-efficacy} atau {selfefficacy, self-directed learning}, dan seterusnya sampai k-item set dengan k sama dengan jumlah item berbeda pada basis data. Dilanjutkan oleh Karthik & Saira Banu (2020) Adapun langkah penelitian secara umum dengan teknik algoritma apriori di dalamnya adalah sebagai berikut: 1. Persiapan data Kegiatan yang dilakukan pada tahap persiapan data adalah (1) fiksasi instrumen yang meliputi proses perancangan dan validasi; dan (2) pengumpulan data yang terdiri atas penentuan populasi dan sampel, waktu dan tempat pengambilan data dan teknis pengumpulan data.…”

Section: Metode Penelitianunclassified

Penelusuran Pola Asosiasi Penalaran Adaptif Dengan Algoritma Apriori

Khotimah

Supriani

Oktaviyanthi

2022

AJPM

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Penalaran adaptif adalah salah satu komponen kunci dari kemahiran matematika. Dalam perkembangannya diketahui penalaran adaptif didukung oleh aspek non-kognitif individu yakni affective dan behavioral. Sejumlah teori mengungkapkan peluang optimalisasi aspek non-kognitif untuk pengembangan kemampuan penalaran adaptif. Tujuan penelitian ini adalah menelusuri faktor nonkognitif yang berpeluang memberi dampak pada optimalisasi kemampuan penalaran adaptif melalui pola asosiasi yang muncul pada basis data. Pendekatan penelitian data mining dengan teknik penemuan pola asosiasi (Association Rule Mining) atau Algoritma Apriori digunakan sebagai metode penelusuran faktor. Langkah penelitian yang dilakukan yakni persiapan data (fiksasi instrumen dan penentuan sampel penelitian), pengolahan data (preprocess dan transformation), dan mining dan analisa (menentukan nilai support dan nilai confidence serta interpretasi hasil). Subjek penelitian yakni siswa Sekolah Menengah Atas di lingkungan Kota Serang yang memiliki rentang usia 16-17 tahun. Adapun untuk sampel penelitian diambil sebanyak 100 orang siswa yang mengisi tes kognitif dan non-kognitif. Berdasarkan hasil pengujian dan analisis data diketahui bahwa seorang siswa memiliki kemungkinan 100% berada di level tinggi kemampuan penalaran adaptif jika memiliki dominasi penggunaan otak kiri, self-efficacy di level yang minimal sedang, kecenderungan kepribadian Melankolis dan self-directed learning berada di level tinggi.Kata kunci: Algoritma apriori; aspek non-kognitif; penalaran adaptif; pola asosiasi.

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Section: Metode Penelitianunclassified

Penelusuran Pola Asosiasi Penalaran Adaptif Dengan Algoritma Apriori

Khotimah

Supriani

Oktaviyanthi

2022

AJPM

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“…The cyclone foraging process comprises of a flocked manta ray that follows both the current and preceding manta ray for ensuring the uniformity of the chain formed. The distinctive progress of the manta ray is obtained in Equation (12).…”

Section: Phase 2: Cyclone Foraging Processmentioning

confidence: 99%

“…In social media mining processes, the three foremost issues, namely, the noise, dynamism, and size are managed by the data mining technique. [12][13][14][15] Social networking with a large number of datasets requires a well programmable data that analyze and deals with it at certain duration of time. 16 Thus the social networking provides a better platform for mining the most significant pattern by employing data mining techniques from huge datasets.…”

Section: Introductionmentioning

confidence: 99%

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Association rule mining based fuzzy manta ray foraging optimization algorithm for frequent itemset generation from social media

Lakshmi¹,

Krishnamurthy²

2021

Concurrency and Computation

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Nowadays, the concept of data mining is employed widely and created a great deal of attention due to its fast arrival. Numerous approaches to frequent itemsets and association rule mining (ARM) are exemplified in recent years, but still, the performances based on scalability and processing time are considered as a major drawback that results in obtaining the solutions with very poor quality. To overcome such shortcomings, this article proposes three significant phases, namely, the data pre-processing phase, data pre-processing, frequent itemset mining, and ARM. In data pre-processing phase, the collected twitter datasets are pre-processed to eliminate redundant data and convert them into an appropriate format for further mining. In the frequent itemset mining phase, an Apriori algorithm is employed for the exact mining of frequent itemsets. The ARM phase utilizes the fuzzy manta ray foraging (FMRF) optimization algorithm that involves the generation of association rules from the huge itemsets thereby achieving minimum confidence and minimum support value. Here, the recent tweets regarding Covid-19, trump2020, joebiden, draintheswamp, and Godzilla are the datasets collected from the Twitter web link. The experimental analysis and the comparative performances are performed for various simulation measures and the results reveal that the proposed approach provides effective performances when compared with various other existing approaches.

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