Comparison of Priori and FP-Growth Algorithms in Determining Association Rules

Islamiyah, None; Ginting, Putri Lestari; Dengen, Nataniel; Taruk, Medi

doi:10.1109/iceeie47180.2019.8981438

Cited by 14 publications

(6 citation statements)

References 10 publications

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“…Support and confidence parameters are commonly used to assess the validity of an association. Apriori [45] and Fp-growth [46] are two popular algorithms for obtaining association rule mining, with Fp-growth being faster due to its use of a compact data structure called a tree [2], [47].…”

Section: Association Rule Mining Designmentioning

confidence: 99%

WIPE: A Novel Web-Based Intelligent Packaging Evaluation via Machine Learning and Association Mining

Tavasoli,

Lee,

Mousavi

et al. 2024

IEEE Access

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This paper introduces the Web-Based Intelligent Packaging Evaluation (WIPE) platform, a novel approach to assess the performance of product and packaging systems within the e-commerce distribution sector. Unlike traditional methods that primarily rely on laboratory evaluations under controlled conditions, WIPE addresses the unique challenges posed by e-commerce distribution, such as increased handling points and unforeseen hazards that standard physical tests may not capture. Leveraging advanced machine learning algorithms and association rule mining, WIPE extracts insights about packaging defects directly from customer reviews on e-commerce platforms. Analyzing both images and text from these reviews establishes connections between frequently used words and the predicted damages, causes, and effects. This innovative approach was exemplified in two case studies involving laundry detergent liquid bottles and pods sold on the Amazon. The findings from these studies demonstrate WIPE's capability to extract pertinent information from customer feedback and identify specific packaging defects and predict their potential causes. This integration of sentiment analysis and association rule mining into the packaging evaluation process marks a significant advancement in the field. The introduction of WIPE represents a transformative step in packaging evaluation, offering a more dynamic, real-world analysis that can significantly enhance product and packaging design, ultimately leading to improved customer satisfaction in the rapidly evolving e-commerce landscape.

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Section: Association Rule Mining Designmentioning

confidence: 99%

WIPE: A Novel Web-Based Intelligent Packaging Evaluation via Machine Learning and Association Mining

Tavasoli,

Lee,

Mousavi

et al. 2024

IEEE Access

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“…Pada penerapkan Association rules terdapat beberapa algoritma yang bisa digunakan untuk menganalisis data transaksi [5], pada penelitian ini menggunakan algoritma FP-Growth. Beberapa penelitian yang dilakukan oleh peneliti sebelumnya mengenai algoritma FP-Growth, menyimpulkan bahwa kinerja algoritma FP-Growth lebih baik dalam menghasilkan aturan asosiasi dan waktu yang dibutuhkan untuk eksekusi lebih cepat dibandingkan dengan algoritma Apriori [6].…”

Section: Pendahuluanunclassified

“…Pada penelitian ini menggunakan parameter support dengan menetapkan nilai minimum 4 % dan parameter confidence dengan menetapkan nilai minimum sebesar 19%. Dari hasil perbandingan kedua algoritma tersebut pada penelitian ini bahwa algoritma Apriori memperoleh aturan asosiasi lebih banyak daripada FP-Growth yaitu Apriori menghasilkan 11 aturan dan FP-Growth 10 aturan, namun waktu eksekusi dengan algoritma FP-Growth lebih cepat dibandingkan dengan Apriori [6].…”

Section: Tinjauan Pustaka 21 Penelitian Terdahuluunclassified

Penerapan Algoritma Fp-Growth Pada Data Transaksi Penjualan Untuk Menentukan Pola Pembelian Pelanggan

Sari,

Faqih,

Anwar

2024

jati

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Dalam dunia bisnis, terutama dibidang penjualan, keberadaan persaingan yang tinggi menimbulkan kebutuhan bagi pengembang dalam mencari strategi yang bisa meningkatkan efektivitas pemasaran dan penjualan produk. Toko Kenzi Olshop merupakan salah satu pebisnis yang bergerak dalam penjualan pakaian, kosmetik dan lain-lain. Namun dalam konteks toko Kenzi Olshop, masih terdapat kendala dalam mengetahui barang apa yang harus diadakan untuk persediaan stok dan promosi serta penempatan produk yang masih kurang efektif khususnya pada produk pakaian. Tujuan dari penelitian ini untuk mengetahui pola pembelian pelanggan, sehingga didapatkan rekomendasi strategi penjualan. Metode yang digunakan adalah knowledge discovery in databases (KDD) dengan menerapkan algoritma FP-Growth. Hasil penelitian ini menunjukkan bahwa algoritma FP-Growth dapat mengidentifikasi pola pembelian pelanggan dengan melakukan percobaan menggunakan nilai minimum support sebesar 2%, 3%, 4%, 5% dan minimum confidence sebesar 40%. Percobaan dengan nilai minimum support 2% dan nilai minimum confidence 40% menghasilkan nilai lift tertinggi dan menghasilkan aturan asosiasi terbanyak yaitu 27 rules. Pada aturan asosiasi tersebut terdapat enam item yang sering muncul meliputi Midi Dress, Pajamas Anisa, Gamis Aisyah, Pajamas Cp, Pajamas Aisyah dan Pajamas Rasya. Serta didapatkan rule dengan nilai confidence terbesar 60,5% yaitu jika konsumen membeli Gamis Aisyah dan Tunik maka memiliki peluang untuk membeli Midi Dress.

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“…Jurnal Inovasi Vokasional dan Teknologi P-ISSN: 1411-3414 E-ISSN: Previous studies have reported that the FP-Growth Algorithm demonstrates superior performance in terms of processing speed compared to the Apriori Algorithm [12]- [14]. As the behavior of algorithms can vary concerning different datasets [15], both FP-Growth and Apriori algorithms were employed to examine their performance differences on the given dataset. Further, [14] and [16] emphasize the importance of context-specific evaluation of these algorithms, pointing out that factors such as the number of transactions and the average transaction width can dramatically influence algorithm performance.…”

Section: N V O T E Kmentioning

confidence: 99%

Exploring the Depths of Market Basket Analysis: A Comprehensive Guide to Transaction Analysis with FP-Growth and Apriori Algorithms

Pratama

2024

INVOTEK

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This research investigates the role of data science in understanding customer behavior and enhancing sales, focusing specifically on the application of Apriori and FP-Growth Algorithms at a retail store, Deli Point, in Labuan Bajo. It illuminates the impact of 'rubbish data' on transactional data analysis, emphasizing the need for robust data cleaning procedures to ensure accurate results. Utilizing the faster FP-Growth Algorithm, the study effectively analyzed customer purchasing patterns to identify optimal product combinations for sales improvement. It discovered that 'parsley local' and 'mint flores' items had the highest support with a value of 0.036, indicating that strategic placement of these items together could enhance sales. The rule between chicken leg bone, orange sunkist, and chicken breast boneless was found to have a high confidence value and a lift value higher than 1, implying a higher potential for these items to be sold when positioned near each other. This study contributes to understanding consumer behavior and provides insights for enhancing sales and competitiveness in the retail industry. An association rule involving 'chicken leg bone’, 'orange sunkist', and 'chicken breast boneless' demonstrated high confidence and a lift value above one, suggesting significant sales potential when these items are grouped together. This study not only contributes valuable insights into retail consumer behavior and effective product placement strategies but also underscores the transformative role of data science in optimizing sales and boosting competitiveness in the retail sector.

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Comparison of Priori and FP-Growth Algorithms in Determining Association Rules

Cited by 14 publications

References 10 publications

WIPE: A Novel Web-Based Intelligent Packaging Evaluation via Machine Learning and Association Mining

WIPE: A Novel Web-Based Intelligent Packaging Evaluation via Machine Learning and Association Mining

Penerapan Algoritma Fp-Growth Pada Data Transaksi Penjualan Untuk Menentukan Pola Pembelian Pelanggan

Exploring the Depths of Market Basket Analysis: A Comprehensive Guide to Transaction Analysis with FP-Growth and Apriori Algorithms

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