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An esteemed maximum utility pattern mining: special children assessment analysis

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Abstract

High utility mining is the act of looking at enormous available databases for identifying the new most valid information. Contrasted with existing utility pattern mining figures the utility of item sets by thinking about the number of items. Past investigations have introduced numerous algorithms for mining high average utility item sets. The limitation of the customary mining algorithm utility is considered as the profit and purchase quantities. An algorithm called high utility mining finds high utility itemsets with various minimum and maximum high average utility counts. Traditional mining methods only consider a positive unit factor for the profit of items. In this paper, Esteemed High Utility Pattern Mining (EHUPM)) algorithm is introduced that mines both non-happening and happening occasions which give more far-reaching results. Mining is done in the assessment database of special children to find information or activities which are most important for their next level. Considering the utility as units that achieved in assessment and unit value given based on vocational training of special children from which child activity is decided. The research further takes the mining work of important factors from the developmental scales of a kid, which is decided from their previous assessment activities. This can help the trainer to concentrate the activities to be trained the group of special children for their higher secondary period to reach vocational training for their next level in special schools.

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Authors and Affiliations

  1. Sathyabama Institute of Science and Technology, Chennai, India

    R. Dhanalakshmi & R. Aroulcanessane

  2. United Institute Of Technology, Coimbatore, India

    B. Muthukumar

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  1. R. Dhanalakshmi
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  2. B. Muthukumar
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  3. R. Aroulcanessane
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Correspondence to R. Dhanalakshmi.

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Dhanalakshmi, R., Muthukumar, B. & Aroulcanessane, R. An esteemed maximum utility pattern mining: special children assessment analysis. Prog Artif Intell 12, 107–117 (2023). https://doi.org/10.1007/s13748-021-00254-2

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