Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth Algorithm

Thurachon, Wannasiri; Kreesuradej, Worapoj

doi:10.1109/access.2021.3071777

Cited by 29 publications

(10 citation statements)

References 35 publications

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“…The dynamic prefix tree approach was suggested as an efficient solution to this problem, although it may require significant computational resources to manipulate the prefix tree [31]. Another approach is for incremental association rule mining, which reduces unnecessary sub-tree construction and rescans of the original database [32]. However, the performance of this approach on very large or complex databases is unclear.…”

Section: Discussion and Analysismentioning

confidence: 99%

An analysis and literature review of algorithms for frequent itemset mining

2023

IJACR

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Section: Discussion and Analysismentioning

confidence: 99%

An analysis and literature review of algorithms for frequent itemset mining

2023

IJACR

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“…Asociation Rule merupakan teknik Data Mining yang menjadi dasar dari berbagai Metode Data Mining lainnya. Asociation Rule digunakan untuk menentukan korelasi antar item dalam suatu kumpulan data yang telah ditentukan [5]. Untuk mendapatkan Association Rule dapat diambil dari Database transaksi untuk mendapatkan dukungan jumlah item yang sering muncul dan item yang sering diperoleh atau dibeli secara bersamaan [6].…”

Section: Pendahuluanunclassified

Identifikasi Pola Penjualan Barang dalam Menjaga Stabilitas Stok Menggunakan Algoritma Fp- Growth

Nurhaida¹

2022

INFEB

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To take advantage of a very large collection of databases, a method or technique is needed that can convert a myriad of data into useful information, one of the data that can be processed is sales data at the Kamang Mart Mini Market. Kamang mart mini market is a mini market that will meet the needs of the community. As an effort to support efficient services, an orderly and thorough work procedure is needed so that it will produce fast, accurate and timely information according to the needs of consumers or the community. To facilitate the mini market in managing data, a system is needed that can produce a decision to find out which products are most in demand and needed by consumers and predict the level of stock availability. So that a lot of data can be used optimally so that later the goods needed by consumers can be fulfilled properly by the mini market and can increase sales at the Kamang Mart minimarket and can also reduce the accumulation of goods that are less desirable by consumers. The transaction data that will be processed in this study are as many as 20 transaction data. The transaction data will be examined using one of the Data Mining techniques by association rule using the Fp-Growth algorithm with a minimum support value of 30% and a confidence value of 70%. So that the pattern of product purchases is obtained which is used as information to predict the level of stock availability of goods. This research is very appropriate to be used by supermarkets in order to convey information more quickly and accurately so that sales levels are increased and well controlled.

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“…Support is related to historical data capacity to establish a rule; confidence, how a certain rule works, and the lift is the ratio between confidence and support. There are several algorithms for association rule mining, such as Apriori [1], FPGrowth [25], Predictive Apriori [47], FPMax [24], DC-miner [7], and FIUFP-Growth [52]. The input is a collection of transactions (in this work, a collection of T), and each transaction contains one or more items (i.e., positional data) obtained from a (1)…”

Section: Generation Of Association Rulesmentioning

confidence: 99%

Sport action mining: Dribbling recognition in soccer

Barbon

Pinto

Barroso

et al. 2021

Multimed Tools Appl

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Recent advances in Computer Vision and Machine Learning empowered the use of image and positional data in several high-level analyses in Sports Science, such as player action classification, recognition of complex human movements, and tactical analysis of team sports. In the context of sports action analysis, the use of positional data allows new developments and opportunities by taking into account players' positions over time. Exploiting the positional data and its sequence in a systematic way, we proposed a framework that bridges association rule mining and action recognition. The proposed Sports Action Mining (SAM) framework is grounded on the usage of positional data for recognising actions, e.g., dribbling. We hypothesise that different sports actions could be modelled using a sequence of confidence levels computed from previous players' locations. The proposed method takes advantage of an association rule mining algorithm (e.g., FPGrowth) to generate displacement sequences for modelling actions in soccer. In this context, transactions are sequences of traces representing player displacements, while itemsets are players' coordinates on the pitch. The experimental results pointed out the Random Forest classifier achieved a balanced accuracy value of 93.3% for detecting dribbling actions, which are considered complex events in soccer. Additionally, the proposed framework provides insights on players' skills and player's roles based on a small amount of positional data.

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Incremental Association Rule Mining With a Fast Incremental Updating Frequent Pattern Growth Algorithm

Cited by 29 publications

References 35 publications

An analysis and literature review of algorithms for frequent itemset mining

An analysis and literature review of algorithms for frequent itemset mining

Identifikasi Pola Penjualan Barang dalam Menjaga Stabilitas Stok Menggunakan Algoritma Fp- Growth

Sport action mining: Dribbling recognition in soccer

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