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Journal articles on the topic 'Frequent Pattern Growth'

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Published: 4 June 2021

Last updated: 25 July 2025

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1

Han, Jiawei, and Jian Pei. "Mining frequent patterns by pattern-growth." ACM SIGKDD Explorations Newsletter 2, no. 2 (2000): 14–20. http://dx.doi.org/10.1145/380995.381002.

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2

S, Abirami. "Pattern-Growth Methods for Frequent Pattern Mining." Shanlax International Journal of Arts, Science and Humanities 6, S1 (2018): 76–81. https://doi.org/10.5281/zenodo.1410989.

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Mining frequent patterns from large databases play an essential role in many data mining tasks and has broad applications. Most of the previously proposed methods adopt Apriori-like candidate-generation-and-test approaches. However, those methods  may  encounter  serious  challenges  when  mining  datasets  with  prolific patterns and long patterns.In this work, to develop a class of novel and efficient pattern-growth methods for mining various frequent patterns from large databases. Pattern-growth methods  adopt a divi

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Suhandi, Nazori, and Rendra Gustriansyah. "Marketing Strategy Using Frequent Pattern Growth." Journal of Computer Networks, Architecture and High Performance Computing 3, no. 2 (2021): 194–201. http://dx.doi.org/10.47709/cnahpc.v3i2.1039.

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The biggest problem faced by printing companies during the Covid-19 pandemic was that the number of orders was unstable and tends to decrease, which had the potential to harm the company. Therefore, various appropriate marketing strategies were needed so that the number of product orders was relatively stable and even increases. The impact was that the company could survive and continued to grow. This study aimed to assist company managers in developing appropriate marketing strategies based on association rules generated from one of the data mining methods, namely the Frequent Pattern Growth

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Bashir, Shariq, and Daphne Teck Ching Lai. "Mining Approximate Frequent Itemsets Using Pattern Growth Approach." Information Technology and Control 50, no. 4 (2021): 627–44. http://dx.doi.org/10.5755/j01.itc.50.4.29060.

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Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional frequent itemset mining. This is because the AFI mining algorithms generate large number of candidate itemsets. This article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution of the proposed approach is it mines core patterns and examines approximate conditions of candidate AFIs directly with single phase and two full scans of database. Related algorithms apply Apriori-based candidate generation and test approach and require multiple phases

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Asyahri, Hadi Nasyuha, Jama Jalius, Abdullah Rijal, et al. "Frequent pattern growth algorithm for maximizing display items." TELKOMNIKA Telecommunication, Computing, Electronics and Control 19, no. 2 (2021): pp. 390~396. https://doi.org/10.12928/TELKOMNIKA.v19i2.16192.

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Products are goods that are available and provided in stores for sale. Products provided in stores must be arranged properly to order to attract the attention of consumers to buy. Products arranged in a store will depend on the type of store. The product arrangement at a retail store will be different from the product arrangement at a clothing store. Store display will reflect a picture that is in the store so consumers know the types of products sold by product arrangement. An attractive arrangement will stimulate the desire of consumers to buy. In data mining there are several types of metho

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Sharma, Rahul, and Dr Manish Manoria. "Novel Approach for Frequent Pattern Algorithm for Maximizing Frequent Patterns in Effective Time." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 2 (2012): 279–83. http://dx.doi.org/10.24297/ijct.v3i2b.2876.

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The essential aspect of mining association rules is to mine the frequent patterns. Due to native difficulty it is impossible to mine complete frequent patterns from a dense database. FP-growth algorithm has been implemented using an Array-based structure, known as the FP-tree,which is for storing compressed frequency information. Numerous experimental results have demonstrated that the algorithm performs extremely well. But in FP-growth algorithm, two traversals of FP-tree are needed for constructing the new conditional FP-tree. In this paper we present a novel Array Based Without Scanning Fre

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Achar, Avinash, Ibrahim A, and P. S. Sastry. "Pattern-growth based frequent serial episode discovery." Data & Knowledge Engineering 87 (September 2013): 91–108. http://dx.doi.org/10.1016/j.datak.2013.06.005.

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Joseph, Jismy, and Kesavaraj G. "Evaluation of Frequent Itemset Mining Algorithms-Apriori and FP Growth." International Journal of Engineering Technology and Management Sciences 4, no. 6 (2020): 1–4. http://dx.doi.org/10.46647/ijetms.2020.v04i06.001.

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Nowadays the Frequentitemset mining (FIM) is an essential task for retrieving frequently occurring patterns, correlation, events or association in a transactional database. Understanding of such frequent patterns helps to take substantial decisions in decisive situations. Multiple algorithms are proposed for finding such patterns, however the time and space complexity of these algorithms rapidly increases with number of items in a dataset. So it is necessary to analyze the efficiency of these algorithms by using different datasets. The aim of this paper is to evaluate theperformance of frequen

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Gunasekaran, G., S. Murugan, and K. Mani. "Auto-Threshold Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation." International Journal of Electrical and Electronics Research 10, no. 3 (2022): 614–19. http://dx.doi.org/10.37391/ijeer.100333.

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Discovering patterns from large datasets is inevitable in the modern data driven civilization. Many research works, and business models are depending on this data excavation task. An efficient method for identifying and categorizing different data patterns from an exponentially growing database is required to perform a clear data excavation. A set of fresh processes such as Repeat Pattern Finder, Repeat Pattern Table, Repeat Pattern Threshold Analyzer, and Repeat Pattern Node are conceptualized in this work named as Auto-Threshold Dynamic Memory Efficient Frequent Pattern Growth for Data Excav

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Asha, P., and T. Jebarajan. "Efficient Mining Of High Utility Patterns Using Frequent Pattern Growth Algorithm." i-manager's Journal on Software Engineering 8, no. 2 (2013): 32–36. http://dx.doi.org/10.26634/jse.8.2.2537.

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Jiten, Gudkha Ashish Patel Swapnil More. Project Guide –. Prof. Christi Lopez. "COMPRESSED FREQUENT PATTERN TREE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 652–57. https://doi.org/10.5281/zenodo.557202.

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The use of Data mining is increasing very rapidly as daily analysis of transaction database consisting of data is increasing. In that data, there ae various item which occur frequently in same pattern. In data mining there are large number of algorithm which are available and used for finding the frequent pattern. In the existing system the algorithm used are Apriori and FP-Growth. The result obtained from such algorithm are very time consuming and not efficient. In proposed system we are using more compact data structure named Compressed FP Tre. We proposed a new algorithm CT-PRO which uses t

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Madhusudhanan, Archana, and Shameem S. "Fast Incremental Updating Frequent Pattern Growth algorithm for Mining." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 1419–25. http://dx.doi.org/10.22214/ijraset.2023.54886.

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Abstract: When a new incremental database is added to an existing database, certain existing frequent item sets may become infrequent item sets, and vice versa. This is one of the most difficult tasks in association rule mining. As a result, certain old association rules may become invalid, while others may become legitimate. It's possible that rules will arise. For incremental association rule mining, we devised a new, more efficient method. A new Incremental Updating Frequent Pattern growth algorithm (FIUFP-Growth) was developed using a Fast Incremental Updating Frequent Pattern growth algor

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Nasyuha, Asyahri Hadi, Jalius Jama, Rijal Abdullah, et al. "Frequent pattern growth algorithm for maximizing display items." TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no. 2 (2020): 390. http://dx.doi.org/10.12928/telkomnika.v19i2.16192.

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Cagliero, Luca, and Paolo Garza. "Infrequent Weighted Itemset Mining Using Frequent Pattern Growth." IEEE Transactions on Knowledge and Data Engineering 26, no. 4 (2014): 903–15. http://dx.doi.org/10.1109/tkde.2013.69.

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Ardiantoro, L., and N. Sunarmi. "Badminton player scouting analysis using Frequent Pattern growth (FP-growth) algorithm." Journal of Physics: Conference Series 1456 (January 2020): 012023. http://dx.doi.org/10.1088/1742-6596/1456/1/012023.

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Xia, Dawen, Xiaonan Lu, Huaqing Li, Wendong Wang, Yantao Li, and Zili Zhang. "A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data." Complexity 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/2818251.

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Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequ

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Isakkson, OGP, J.-O. Jansson, RG Clark, and I. Robinson. "Significance of the Secretory Pattern of Growth Hormone." Physiology 1, no. 2 (1986): 44–47. http://dx.doi.org/10.1152/physiologyonline.1986.1.2.44.

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The plasma concentration of growth hormone fluctuates widely with pronounced peaks at intervals of a few hours and troughs of nearly vanishingly low concentrations in between. The pattern of secretion varies, and different patterns affect growth differently. Tall children usually have frequent growth hormone peaks of a high amplitude, whereas short, healthy children usually have fewer peaks of a lower amplitude. Male and female rats have different patterns, and a "masculine" pattern promotes growth more than a "feminine" pattern. If the same amount of growth hormone is administered in several

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18

Wisaeng, Kittipol. "Association rule with frequent pattern growth algorithm for frequent item sets mining." Applied Mathematical Sciences 8 (2014): 4877–85. http://dx.doi.org/10.12988/ams.2014.46432.

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Steven, Steven, Viny Christanti Mawardi, and Tri Sutrisno. "SISTEM REKOMENDASI PAKET MINUMAN BERDASARKAN PESANAN PELANGGAN DENGAN METODE FREQUENT PATTERN GROWTH." Jurnal Ilmu Komputer dan Sistem Informasi 8, no. 1 (2020): 165. http://dx.doi.org/10.24912/jiksi.v8i1.11490.

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It is proposed that a beverage package is determined based on customer orders by looking for different frequent itemset patterns in the transaction data that has occurred. These data can be analyzed to be more useful. The method used is the association method. With the predicted beverage package the company can increase sales. With so many transaction data stored, the difficulty in managing data requires a method called the association method. Frequent Pattern Growth Algorithm is an alternative algorithm that is quite effective for finding the set of data that most often appears (frequent item

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Lu, Ping-Hsun, Jui-Lin Keng, Fu-Ming Tsai, Po-Hsuan Lu, and Chan-Yen Kuo. "An Apriori Algorithm-Based Association Rule Analysis to Identify Acupoint Combinations for Treating Diabetic Gastroparesis." Evidence-Based Complementary and Alternative Medicine 2021 (March 25, 2021): 1–9. http://dx.doi.org/10.1155/2021/6649331.

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We explored the potential association rules within acupoints in treating diabetic gastroparesis (DGP) using Apriori algorithm complemented with another partition-based algorithm, a frequent pattern growth algorithm. Apriori algorithm is a data mining-based analysis that is widely applied in various fields, such as business and medicine, to mine frequent patterns in datasets. To search for effective acupoint combinations in the treatment of DGP, we implemented Apriori algorithm to investigate the association rules of acupoints among 17 randomized controlled trials (RCTs). The acupoints were ext

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Wisda, Wisda, and Mashud Mashud. "Designing an Application for Analyzing Consumer Spending Patterns Using the Frequent Pattern Growth Algorithm." Jurnal Penelitian Pos dan Informatika 9, no. 2 (2019): 151. http://dx.doi.org/10.17933/jppi.2019.090206.

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<p class="JGI-AbstractIsi">In this modern era, the market has been growing rapidly which can be seen from the navel shopping that is lined up in the hearts of big cities such as supermarkets, grocery stores and others that are provided to meet people's needs for primary goods that are always needed at all times. One of them is Giant Express Tamalanrea, a supermarket in the city of Makassar that serves the sale of household goods and general needs. With the use of customer data analysis to determine the customers' purchasing patterns, Giant Express can optimize the collation of goods, by

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Wisda, Wisda, and Mashud Mashud. "Designing an Application for Analyzing Consumer Spending Patterns Using the Frequent Pattern Growth Algorithm." Jurnal Penelitian Pos dan Informatika 9, no. 2 (2019): 151–59. http://dx.doi.org/10.17933/jppi.v9i2.285.

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In this modern era, the market has been growing rapidly which can be seen from the navel shopping that is lined up in the hearts of big cities such as supermarkets, grocery stores and others that are provided to meet people's needs for primary goods that are always needed at all times. One of them is Giant Express Tamalanrea, a supermarket in the city of Makassar that serves the sale of household goods and general needs. With the use of customer data analysis to determine the customers' purchasing patterns, Giant Express can optimize the collation of goods, by positioning goods at closer shelv

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23

Gunasekaran, G., and S. Murugan. "Dynamic Memory Efficient Frequent Pattern Growth for Data Excavation." International Journal of Computer Applications 179, no. 7 (2017): 32–40. http://dx.doi.org/10.5120/ijca2017915973.

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24

Rizkia Amelia, Abdul Koda, Herawati, Iis Mulyati, and Raditya Danar Dana. "Penerapan Algoritma Frequent Pattern-Growth Dalam Menentukan Pola Penjualan." KOPERTIP : Jurnal Ilmiah Manajemen Informatika dan Komputer 4, no. 2 (2022): 65–71. http://dx.doi.org/10.32485/kopertip.v4i2.121.

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Penggalian suatu informasi penting digunakan oleh setiap perusahaan dalam memanfaatkan penyimpanan data yang ada. Hal tersebut juga digunakan oleh PT. Sinar Agung Prasadikindo untuk mengetahui pola penjualan spare part. belum adanya pemanfaatan data transaksi penjualan dalam mengetahui pola penjualan spare part dalam mengetahui spare part mana yang paling banyak diminati. Hal tersebut sering menimbulkan permasalahan dalam pemesanan spare part pada kantor pusat karena tidak sesuai dengan kenyataan dilapangan.Untuk mengatasi permasalahan tersebut dapat dilakukan dengan menggunakan penerapan algo

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Retnoningsih, Endang, and Tia Monisya Afriyanti. "Sistem Rekomendasi Buku Perpustakaan Menggunakan Algoritma Frequent Pattern Growth." Techno.Com 21, no. 2 (2022): 292–310. http://dx.doi.org/10.33633/tc.v21i2.5789.

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Perpustakaan memiliki pelayanan utama memfasilitasi peminjaman buku, untuk memudahkan anggota perpustakaan menemukan buku yang tepat, perpustakaan dapat dilengkapi dengan sistem pencarian buku. Sistem pencarian buku yang tersedia umumnya kurang membantu untuk menemukan buku yang tepat bagi anggota yang belum menentukan buku yang akan dipinjam. Algoritma Frequent Pattern Growth (FP-Growth) merupakan metode yang digunakan dalam sistem rekomendasi peminjaman buku. Tahapan dari metode FP-Growth yaitu tahap pengumpulan data, tahap menyusun tabel generate frequent itemset, tahap menentukan nilai min

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Putra, Rezi Elsya, Asmar Yulastri, Genefri Genefri, and Mohd Iqbal. "Analisis Sistem Frequent Pattern Growth Untuk Penjualan Produk Herbal." JRST (Jurnal Riset Sains dan Teknologi) 7, no. 1 (2023): 65. http://dx.doi.org/10.30595/jrst.v7i1.16527.

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Perkembangan teknologi memberikan dampak yang besar terhadap perkembangan dunia bisnis. Persaingan dalam dunia bisnis sangat ketat sehingga perlu melihat potensi transaksi penjualan produk dan memiliki strategi penjualan produk yang tepat. HNI Business Center 2 merupakan salah satu toko yang menjual produk herbal. Dengan banyaknya permintaan obat herbal pada saat covid-19 maka diperlukan stok obat guna meningkatkan penjualan produk. Maka diperkulan suatu anlisis untuk membantu pimpinan HNI Business Center 2 mengetahu produk yang harus ditingkatkan stoknya sesuai dengan data transaksi pelanggan

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Patel, Sanjay, and Dr Ketan Kotecha. "Incremental Frequent Pattern Mining using Graph based approach." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (2005): 731–36. http://dx.doi.org/10.24297/ijct.v4i2c2.4191.

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Extracting useful information from huge amount of data is known as Data Mining. It happens at the intersection of artificial intelligence and statistics. It is also defined as the use of computer algorithms to discover hidden patterns and interesting relationships between items in large datasets. Candidate generation and test, Pattern Growth etc. are the common approaches to find frequent patterns from the database. Incremental mining is a crucial requirement for the industries nowadays. Many tree based approaches have tried to extend the frequent pattern mining as an incremental approach, but

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Shashidhar, Keerthan, Kuttappa M N, U. S. Krishna Nayak, Neevan D'Souza, Mahabalesh Shetty, and Sonika Achalli. "Association between dermatoglyphic patterns and growth patterns of subjects with skeletal class I relation: A cross sectional study." F1000Research 11 (June 1, 2022): 597. http://dx.doi.org/10.12688/f1000research.121961.1.

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Background: To assess the relationship between dermatoglyphic patterns and various growth patterns of the mandible. Methods: Patients with Class I Skeletal relation were selected after clinical diagnosis followed by digitally tracing the cephalogram. The patients were subdivided into three groups of mandibular divergence patterns ie Average, Horizontal and Vertical. 90 samples ie 30 in each group were selected for the study. The fingerprints of all the selected subjects were then extracted digitally and analysed for the most dominant pattern in each hand. Results: For the left hand, there was

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Shashidhar, Keerthan, Kuttappa M N, U. S. Krishna Nayak, Neevan D'Souza, Mahabalesh Shetty, and Sonika Achalli. "Association between dermatoglyphic patterns and growth patterns of subjects with skeletal class I relation: A cross sectional study." F1000Research 11 (July 4, 2022): 597. http://dx.doi.org/10.12688/f1000research.121961.2.

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Background: To assess the relationship between dermatoglyphic patterns and various growth patterns of the mandible. Methods: Patients with Class I Skeletal relation were selected after clinical diagnosis followed by digitally tracing the cephalogram. The patients were subdivided into three groups of mandibular divergence patterns ie Average, Horizontal and Vertical. 90 samples ie 30 in each group were selected for the study. The fingerprints of all the selected subjects were then extracted digitally and analysed for the most dominant pattern in each hand. Results: For the left hand, there was

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Nofianti, Erlina, Wiwit Agus Triyanto, and Noor Latifah. "PENENTUAN STRATEGI PEMASARAN MENGGUNAKAN FREQUENT PATTERN GROWTH (FP-GROWTH) PADA TOKO KOMPUTER." Indonesian Journal of Technology, Informatics and Science (IJTIS) 1, no. 2 (2020): 59–62. http://dx.doi.org/10.24176/ijtis.v1i2.4941.

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Transaksi pemasaran pada Rusdianto Komputer yang banyak setiap hari menghasilkan data tertumpuk pada excel, meskipun tertumpuknya data sudah diarsipkan akan tetapi belum dimanfaatkan untuk penentuan strategi pemasaran. Data Mining merupakan proses penambangan suatu informasi yang terdapat pada himpunan data besar sehingga bermanfaat untuk mendapatkan pengetahuan mutakhir dan bermanfaat dalam mengambil keputusan. Ketatnya dunia persaingan pemasaran PC pada toko komputer, diperlukanlah suatu metode yang terbaik bagi Rusdianto Komputer dalam menentukan strategi pemasaran dengan memanfaatkan keter

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Erlinda, Erlinda, Dwipa Junika Putra, and Mourend Devegi. "Student Identification Based on Patterns of Association For Student Misbehaviour Using Frequent Pattern Growth Algorithms." JURNAL TEKNOLOGI DAN OPEN SOURCE 6, no. 1 (2023): 142–50. http://dx.doi.org/10.36378/jtos.v6i1.3071.

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Student infractions are incidents often committed by students who break the rules at school. This naturally worries school authorities and overwhelms them with student misbehavior. Student rule-breaking is a common problem that can interfere with a safe and orderly learning environment. The more students break the rules, the greater the impact on several aspects, including student achievement, discipline, suboptimal teaching and learning activities, and students' social lives outside of school. Identifying students who are prone to rule violations can help school officials implement more effec

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Spits Warnars, Harco Leslie Hendric. "Using Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) to Mine Frequent Patterns." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (2016): 3037. http://dx.doi.org/10.11591/ijece.v6i6.10579.

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<p><span lang="EN-US">Frequent patterns in Attribute Oriented Induction High level Emerging Pattern (AOI-HEP), are recognized when have maximum subsumption target (superset) into contrasting (subset) datasets (contrasting </span><span lang="EN-US">⊂</span><span lang="EN-US"> target) and having large High Emerging Pattern (HEP) growth rate and support in target dataset. HEP Frequent patterns had been successful mined with AOI-HEP upon 4 UCI machine learning datasets such as adult, breast cancer, census and IPUMS with the number of instances of 48842, 569, 245

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Spits Warnars, Harco Leslie Hendric. "Using Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) to Mine Frequent Patterns." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (2016): 3037. http://dx.doi.org/10.11591/ijece.v6i6.pp3037-3046.

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<p><span lang="EN-US">Frequent patterns in Attribute Oriented Induction High level Emerging Pattern (AOI-HEP), are recognized when have maximum subsumption target (superset) into contrasting (subset) datasets (contrasting </span><span lang="EN-US">⊂</span><span lang="EN-US"> target) and having large High Emerging Pattern (HEP) growth rate and support in target dataset. HEP Frequent patterns had been successful mined with AOI-HEP upon 4 UCI machine learning datasets such as adult, breast cancer, census and IPUMS with the number of instances of 48842, 569, 245

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Priya, A. Selva. "Comparative Analysis of Algorithms for Mining Frequent Patterns." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1288–94. http://dx.doi.org/10.22214/ijraset.2023.53799.

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Abstract: In the computerized world, everything is moving online, and data comes in different shapes and sizes and is collected in different ways. By using data mining, frequent pattern in the databases can be identified, and it can be used in numerous applications. Finding frequent patterns in huge databases is important because it reveals important information that cannot be found through simple data surfing. To find common patterns, a variety of methods are utilized, each of which performs differently. Apriori and FP Growth are the fundamental algorithms employed in frequent pattern mining.

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Noorkholid, Mohammad Ivan, Muhammad Arief Hidayat, and Gama Wisnu Fajarianto. "Sistem Informasi Penentuan Paket Pembelian Produk Menggunakan Algoritma Frequent Pattern-Growth pada KPRI Jember." BERKALA SAINSTEK 8, no. 2 (2020): 59. http://dx.doi.org/10.19184/bst.v8i2.11848.

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Penelitian ini bertujuan untuk merancang dan membangun sistem informasi penentuan paket pembelian produk pada KPRI Jember. Sistem ini menggunakan algoritma Fp-Growth (Frequent Pattern Growth) untuk menghasilkan informasi tentang paket pembelian produk dengan menangkap fenomena yang terjadi dalam transaksi penjualan. Implementasi algoritma Frequent Pattern Growth menggunakan PHP. Data hasil perhitungan algoritma Frequent Pattern Growth divisualisasikan dalam halaman website. Penerapan algoritma Frequent Pattern Growth didukung dengan metode association rules untuk menghasilkan data yang lebih l

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Fageeri, Sallam Osman, Mohammad Abu Kausar, and Arockiasamy Soosaimanickam. "MBA: Market Basket Analysis Using Frequent Pattern Mining Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 15–21. http://dx.doi.org/10.17762/ijritcc.v11i5s.6591.

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This Market Basket Analysis (MBA) is a data mining technique that uses frequent pattern mining algorithms to discover patterns of co-occurrence among items that are frequently purchased together. It is commonly used in retail and e-commerce businesses to generate association rules that describe the relationships between different items, and to make recommendations to customers based on their previous purchases. MBA is a powerful tool for identifying patterns of co-occurrence and generating insights that can improve sales and marketing strategies. Although a numerous works has been carried out

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Purba, Tigor Novanda, and Diky Firdaus. "DETERMINATION FOR CONSUMER PATTERNS IN BEVERAGE PRODUCT SALES USING THE FREQUENT PATTERN GROWTH ALGORITHM." IJISCS (International Journal of Information System and Computer Science) 5, no. 2 (2021): 84. http://dx.doi.org/10.56327/ijiscs.v5i2.982.

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The culinary business is now increasingly developing and competition is increasing, so it requires a strategy to market the products to be sold. In the business sector, the results of the implementation of FP-Growth algorithm data mining can help business people find opportunities from consumption trends so that culinary business people can find out what types of products currently have the highest rating in the community so that managers can provide menu recommendations so they can increase sales turnover. The data required is a certain period of transaction data which is analyzed to produce

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Alam, Md Tanvir, Chowdhury Farhan Ahmed, and Md Samiullah. "A Vertex-extension based Algorithm for Frequent Pattern Mining from Graph Databases." Dhaka University Journal of Applied Science and Engineering 7, no. 1 (2023): 58–65. http://dx.doi.org/10.3329/dujase.v7i1.62887.

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Frequent pattern mining is a core problem in data mining. Algorithms for frequent pattern mining have been proposed for itemsets, sequences, and graphs. However, existing graph mining frameworks follow an edge-growth approach to building patterns which limits many applications. Motivated by real-life problems, in this work, we define a novel graph mining framework that incorporates vertex-based extensions along with the edge-growth approach. We also propose an efficient algorithm for mining frequent subgraphs. To deal with the exploding search space, we introduce a canonical labeling technique

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A.H.M, Sajedul Hoque, Rashed Mustafa, Sujit Kumar Mondal, and Md Al-Amin Bhuiyan. "A Fuzzy Frequent Pattern-Growth Algorithm for Association Rule Mining." International Journal of Data Mining & Knowledge Management Process 5, no. 5 (2015): 21–33. http://dx.doi.org/10.5121/ijdkp.2015.5502.

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Lin, Ke-Chung, I.-En Liao, and Zhi-Sheng Chen. "An improved frequent pattern growth method for mining association rules." Expert Systems with Applications 38, no. 5 (2011): 5154–61. http://dx.doi.org/10.1016/j.eswa.2010.10.047.

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Parulian, L. Taufik, Rouly Doharma, and Ahmad Taufik. "PREDIKSI PERMINTAAN MATA KULIAH SEMESTER ANTARA (PADAT) DENGAN MENGGUNAKAN ALGORITMA FOLD-GROWTH DAN FP-GROWTH." Infotech: Journal of Technology Information 9, no. 1 (2023): 51–58. http://dx.doi.org/10.37365/jti.v9i1.159.

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The intermediate semester is the lecture period which is held during the holidays between even and odd semesters which is one of the alternatives given to students. The main purpose of predicting the demand for intermediate semester courses is to be able to help students make improvements to less than optimal grades and to be able to shorten the lecture period. All forms of preparation, both schedules and lecturers' appointments made by the higher education institution are related to requests for courses to be followed by students. Frequent Pattern-Growth (FP-Growth) is an alternative algorith

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Oyekunle, Victoria Oluwatoyin, Mercy Nwanyanwu, and Mercy Azibaye Ide. "Efficient Method of Mining Sequential pattern in retail database." Journal of Scientific and Engineering Research 8, no. 5 (2021): 65–74. https://doi.org/10.5281/zenodo.10590173.

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<strong>Abstract</strong> There is difficulty in detecting repetitive behavior for a company in order to identify regularities in businesses and inability to determine meta patterns as a group of events that lead to particular deviations in customers’ behavior. However, a sequential pattern mining mechanism has been developed to discover all sequences and subsequences that are repetitive in a meta data. Percussive method of pattern growth algorithms of Sequential Pattern Mining (SPM) has been used for finding frequent patterns from a huge data set. It first scans the sequence database an

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Musaddad, Anwar, Odi Nurdiawan, and Gifthera Dwilestari. "Penerapan Association Rule Menggunakan Frequent Pattern Growth Untuk Rekomendasi Produk Jersey Sepakbola." Journal of Computer System and Informatics (JoSYC) 3, no. 3 (2022): 100–105. http://dx.doi.org/10.47065/josyc.v3i3.1390.

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The phenomenon of the beginning of the year, what some football fans have been waiting for, is the publication of the latest jersey from their favorite team. When the new jersey was launched, football fans flocked to buy the jersey, but there were several shops available for the new jersey. This was experienced by the Eighteen Sport shop, in fulfilling the wishes of fans, there were obstacles to re-stock the jerseys that were most in demand. So many items that have not been sold. The focus of this research lies in managing jersey sales data in June, July and August, as well as high interest in

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Abdullah, Zailani, Tutut Herawan, A. Noraziah, and Mustafa Mat Deris. "A Scalable Algorithm for Constructing Frequent Pattern Tree." International Journal of Intelligent Information Technologies 10, no. 1 (2014): 42–56. http://dx.doi.org/10.4018/ijiit.2014010103.

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Frequent Pattern Tree (FP-Tree) is a compact data structure of representing frequent itemsets. The construction of FP-Tree is very important prior to frequent patterns mining. However, there have been too limited efforts specifically focused on constructing FP-Tree data structure beyond from its original database. In typical FP-Tree construction, besides the prior knowledge on support threshold, it also requires two database scans; first to build and sort the frequent patterns and second to build its prefix paths. Thus, twice database scanning is a key and major limitation in completing the co

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Kurnia Handayani, Putri, and Nanik Susanti. "ANALISIS KINERJA ALGORITMA FREQUENT PATTERN GROWTH (FP-GROWTH) PADA PENAMBANGAN POLA ASOSIASI DATA TRANSAKSI." Indonesian Journal of Technology, Informatics and Science (IJTIS) 1, no. 1 (2019): 9–12. http://dx.doi.org/10.24176/ijtis.v1i1.4596.

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Data transaksi penjualan yang setiap hari bertambah menyebabkan banjir data dalam database. Data transaksi tersebut hanya digunakan sebagai laporan penjualan yang dicetak setiap bulannya. Data mining merupakan kegiatan menambang/menggali data untuk mengenali pola atau aturan tertentu dari sejumlah dataset yang sangat besar dan mempunyai dimensi tinggi. Asosiasi adalah teknik data mining untuk menemukan aturan suatu kombinasi item. Pola asosiasi yang berhasil diketahui dapat membantu pihak manajemen untuk mendukung pengambilan keputusan berkaitan dengan strategi penjualan, promosi produk, rewar

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Moschonis, George, Andriana C. Kalliora, Vassiliki Costarelli, et al. "Identification of lifestyle patterns associated with obesity and fat mass in children: the Healthy Growth Study." Public Health Nutrition 17, no. 3 (2013): 614–24. http://dx.doi.org/10.1017/s1368980013000323.

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AbstractObjectiveTo investigate possible associations of lifestyle patterns with obesity and fat mass in children.DesignCross-sectional epidemiological study. Principal component analysis was used to identify lifestyle patterns.SettingPrimary schools from four regions in Greece.SubjectsA total of 2073 schoolchildren (aged 9–13 years).ResultsChildren in the fourth quartile of the lifestyle pattern combining higher dairy foods with more adequate breakfast consumption were 39·4 %, 45·2 % and 32·2 % less likely to be overweight/obese and in the highest quartile of sum of skinfold thicknesses and f

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Damanik, Florida Nirma Sanny, Andrew Sagita, Harianto -, and Andy Syaputra. "Aplikasi Pengenalan Pola Pembelian Konsumen Menggunakan Kombinasi Algoritma FP-Growth Dan ECLAT Method (FEM)." Jurnal SIFO Mikroskil 19, no. 2 (2018): 1–12. http://dx.doi.org/10.55601/jsm.v19i2.553.

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Sales data stored in enterprise databases are usually stored as archives or documentation. In the case of retail companies, data mining science can be used to extract new information from sales database, ie consumer purchase pattern analysis. The algorithm that can be used to analyze consumer purchase pattern is FEM algorithm using combination of Frequent Pattern Growth (FP-Growth) and Eclat algorithm. The construction of FP-Tree tree structure is done by using FP-Growth algorithm, while the process of extraction of items purchased (frequent itemset) is done by using Eclat algorithm. The appli

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Kamil, Fakhri, Dony Permana, Dodi Vionanda, and Dina Fitria. "Library Book Lending Recommendation Using Association Rules with Frequent Pattern Growth (FP-Growth) Algorithm." UNP Journal of Statistics and Data Science 2, no. 4 (2024): 453–62. https://doi.org/10.24036/ujsds/vol2-iss4/284.

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College libraries are libraries managed by higher education institutions such as university libraries. The library functions as an information center management forum for students which includes learning resource functions, access functions, librarian functions, ethical functions, and evaluation functions. Along with changes in information search behavior in the era of information technology, libraries continue to try to improve services to meet student needs. Students prefer to read through e-books rather than reading books or library collections. There is a paradigm that is believed by stude

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Van, Trang Thien Thi. "Survey on Sequential Pattern Mining based on User - Constraints." Journal of Development and Integration, no. 73 (December 25, 2023): 68–77. http://dx.doi.org/10.61602/jdi.2023.73.09.

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Sequence data mining, also known as sequential pattern mining, is to find all frequent sub-sequences (called sequential patterns) in a sequence database, the threshold of frequency is specified by the user. In recent years, with the explosion growth of information and big data, this problem trends toward mining with constraints to overcome both effectiveness and efficiency challenges since that the constraints represent for the user’s interest. This paper presents a detailed survey of recent studies on mining sequential pattern and the categories of the constraints. Moreover, it also classifie

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Arour, Khedija, and Amani Belkahla. "Frequent Pattern-growth Algorithm on Multi-core CPU and GPU Processors." Journal of Computing and Information Technology 22, no. 3 (2014): 159. http://dx.doi.org/10.2498/cit.1002361.

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