Survey On Recognition Hand Gesture By Using Data Mining Algorithms

Khalaf, Luay Ibrahim; Aswad, Salma Abdullah; Ahmed, Saadaldeen Rashid; Makki, Bassma; Ahmed, Mohammed Rashid

doi:10.1109/hora55278.2022.9800090

Cited by 53 publications

(1 citation statement)

References 20 publications

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“…From the formulation of the problem described above, there are several objectives of this study, namely knowing consumer buying patterns in each branch, interpreting the patterns that have been generated into information, designing a good sales strategy and system [5], applying the Data Mining method with the FP-Growth algorithm into the consumer buying pattern analysis application, and testing the patterns that have been implemented successfully or not [6].…”

Section: Introductionmentioning

confidence: 99%

Use of Data Mining for The Analysis of Consumer Purchase Patterns with The Fpgrowth Algorithm on Motor Spare Part Sales Transactions Data

Lael

Pramudito²

2023

itsdi

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This study aims to analyze consumer purchasing patterns for motorcycle parts using data mining methods and FP-Growth algorithms on motorcycle parts sales transaction data. This research aims to obtain helpful information for companies in planning marketing strategies and increasing sales. The data used in this study are motorcycle parts sales transaction data from motorcycle parts stores for one year. The data is then processed using the FP-Growth algorithm to find significant purchasing patterns. The results of this study show that the FP-Growth algorithm can be used to identify substantial consumer purchasing patterns. Some purchase patterns found include a combination of often purchased products, the most active purchase time, and the most purchased product category. Using data mining and the FP-Growth algorithm can assist companies in understanding significant consumer purchasing patterns to improve the effectiveness of marketing strategies and increase sales of motorcycle parts. The novelty of this research lies in using data mining methods and FP-Growth algorithms on motorcycle parts sales transaction data to analyze consumer purchasing patterns. This research also provides valuable information for companies in planning marketing strategies and increasing sales by identifying significant consumer purchasing patterns, such as product combinations often purchased together and the most purchased product categories.

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Section: Introductionmentioning

confidence: 99%

Use of Data Mining for The Analysis of Consumer Purchase Patterns with The Fpgrowth Algorithm on Motor Spare Part Sales Transactions Data

Lael

Pramudito²

2023

itsdi

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Evaluation Method of Higher Vocational Online Education Effect Based on Data Mining Algorithm

Niu,

Wang

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Statistical Data Mining Methods in Predicting Happiness and Habits

Sulaiman,

Jghef,

Abdullah

et al. 2024

ITM Web Conf.

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The objective of this study is to employ statistical data mining methods and con-duct a survey among young individuals to construct a model capable of forecasting overall happiness. This model will consider over a hundred characteristics, including lifestyle choices and musical tastes. We utilized boosting trees, subset se-lection, and GAM (Generalized Additive Models) techniques. In addition, we created actual test data to validate the model. All available approaches have found many lifestyle variables, including as energy levels, loneliness, desire to alter the past, eating properly, and spending time with friends, as significant determinants of happiness. We generated authentic test data to verify the model, utilizing rigorous testing protocols to evaluate its predicted precision and applicability across various demographics. Based on our investigation, the use of the gradient boost technique resulted in improved picture projections. The evaluation of the technique using a confusion matrix revealed an accuracy of 97.1% for training and a perfect accuracy of 100% for validation. The training phase achieved an accuracy of 62.5%, as shown by the confusion matrix, while the overall confusion matrix demonstrated a 92.0% accuracy in predicting happiness. The support vector machine, trained incrementally, demonstrated encouraging prospects for future investigation.

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Survey On Recognition Hand Gesture By Using Data Mining Algorithms

Cited by 53 publications

References 20 publications

Use of Data Mining for The Analysis of Consumer Purchase Patterns with The Fpgrowth Algorithm on Motor Spare Part Sales Transactions Data

Use of Data Mining for The Analysis of Consumer Purchase Patterns with The Fpgrowth Algorithm on Motor Spare Part Sales Transactions Data

Evaluation Method of Higher Vocational Online Education Effect Based on Data Mining Algorithm

Statistical Data Mining Methods in Predicting Happiness and Habits

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