FP Growth Algorithm Implementation

Sidhu, Shivam; Meena, Upendra Kumar; Nawani, Aditya; Gupta, Himanshu; Thakur, Narina

doi:10.5120/16233-5613

Cited by 18 publications

(9 citation statements)

References 5 publications

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“…The Association Rule generation method gave way to an improved rule generation technique known as the FP-Growth Algorithm, which was proposed by Han [78]. It is an efficient method wherein the mining is done by an extended prefix-tree structure on a complete set of frequent patterns by patterns fragment growth [79]. The tree structure stores the compressed information about frequent patterns.…”

Section: Fp Growthmentioning

confidence: 99%

BoPCOVIPIP: Capturing the Dynamics of Marketing Mix Among Bottom of Pyramid Consumers during COVID-19

Panda¹,

Mukhopadhyay²,

Saha³

et al. 2022

IJISA

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The behaviour of consumers mostly follows the guidelines derived from marketing theories and models. But under some unavoidable circumstances, the consumers show a complete deviation compared to their existing consumption pattern, purchase behaviour, decision-making and so on. Under similar circumstances, this study aims to capture both urban and rural Bottom of the Pyramid (BoP) consumers’ perceptions of various marketing mixes during the COVID-19 pandemic situation. With a sample size of 378 and 282, the perception towards different marketing mixes has been captured for Pre-COVID and During-COVID periods, respectively. The adopted quantitative analysis indicates a difference in perception towards marketing mix During COVID compared to Pre-COVID. Moreover, the selection of West Bengal, India, as an area of research fulfills the BoP literature’s existing prominent research gap. This study also comes with the potential to assist marketers and the Fast-Moving Consumer Goods (FMCG) industry in framing strategies to target BoP consumers.

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Section: Fp Growthmentioning

confidence: 99%

BoPCOVIPIP: Capturing the Dynamics of Marketing Mix Among Bottom of Pyramid Consumers during COVID-19

Panda¹,

Mukhopadhyay²,

Saha³

et al. 2022

IJISA

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“…Unlike in the Apriori algorithm, there is no need for candidate generation with FP trees, and the frequently occurring item sets are discovered by simply crossing the FP tree [4].…”

Section: Fp-growth Algorithmmentioning

confidence: 99%

Association Rule Analysis of Fp-Growth Algorithm on Drug Purchase Patterns

Putri

2022

tekno

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Salah satu teknik data mining adalah Association Rule yang sering disebut dengan prosedur Market Basket Analysis untuk mencari pengetahuan tentang pola pembelian konsumen. Riset ini menggunakan algoritma Frequent Pattern Growth (FP-Growth). FP-Growth dalam membentuk itemset dilakukan dengan membuat struktur data FP-Tree. Data yang digunakan memanfaatkan transaksi Klinik selama dua tahun. Analisis dilakukan untuk mempertimbangkan keputusan bagi pemangku kepentingan informasi di Klinik. Hasilnya diperoleh 118 rule dengan nilai minimal 30% support dan 75% confidence. Aturan yang dihasilkan 100% jika beli INJ Piralen maka beli INJ Ranitidine, 100% jika beli Genoint maka beli Genoint SK, jika beli Gastritis Cap maka beli 100% Myalgia Cap, jika beli Sanbe SP NACL Liquid maka beli 100% Glucose Dextrose Liquid, kalau beli Omecidal beli Omedeson 100%, dan kalau beli Omeprazole beli Gludepatik 87%. Riset ini membantu pemilik Klinik menentukan pola dan menampilkan obat yang paling laris dibeli konsumen.

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“…FP-Growth algorithm is a structure which is compact and stores quantitative information about frequent pattern in data base. It applies divide and conquer strategy [16,17]. Apriori algorithm is the most popular due to influence's algorithm ability especially for the Boolean association rules and the easy for parallelized [6].…”

Section: Number Of Basket That Contains Bothmentioning

confidence: 99%

Association Rule Mining Based Crime Analysis using Apriori Algorithm

Jantan¹

2019

IJATCSE

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Nowadays, criminal law enforcement is a crucial task due to the increasing of crime rates, limitation of manpower and lack of awareness from the local community. Historical data on crimes activities need to be analyzed in order to get the trend and pattern of crimes for future prevention actions. The aim of this article is to explore the relationship between the category of location and area of criminal records through extracting the patterns that frequently occur by applying Apriori algorithm from Association Rule Mining (ARM) method. CRISP-DM methodology is used to conduct this study that consists of business and data understanding, data preparation, modeling, evaluation and deployment phases. As a result, there are strong rules being created from the high support and confidence in modeling process where the generated rules would be considered as potential rules for pattern visualization in crime analysis. This study brings a high significance for effectiveness and efficiency strategy in criminal law enforcement and it can also be explored for other association rule mining methods for future work enhancement.

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FP Growth Algorithm Implementation

Cited by 18 publications

References 5 publications

BoPCOVIPIP: Capturing the Dynamics of Marketing Mix Among Bottom of Pyramid Consumers during COVID-19

BoPCOVIPIP: Capturing the Dynamics of Marketing Mix Among Bottom of Pyramid Consumers during COVID-19

Association Rule Analysis of Fp-Growth Algorithm on Drug Purchase Patterns

Association Rule Mining Based Crime Analysis using Apriori Algorithm

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